Adaptive knowledge discovery techniques for data mining
dc.contributor.advisor  Purvis, Martin  
dc.contributor.advisor  Cranefield, Stephen  
dc.contributor.advisor  Kasabov, Nikola  
dc.contributor.author  Zhou, Qing Qing  en_NZ 
dc.date.available  20110407T03:17:04Z  
dc.date.copyright  200307  en_NZ 
dc.identifier.citation  Zhou, Q. Q. (2003, July). Adaptive knowledge discovery techniques for data mining (Thesis, Doctor of Philosophy). Retrieved from http://hdl.handle.net/10523/1492  en 
dc.identifier.uri  http://hdl.handle.net/10523/1492  
dc.description.abstract  The ease of collection and the increasing availability of large data stores has led to demands for improved methods for analyzing these data and deriving significant knowledge that maybe latent in these data stores. In particular there is hope that the use of new analytical techniques in connection with "data trawling", or data mining operations may reveal hidden relationships that lie buried within these data sets. This research investigates various techniques for the task of discovering relevant features and inference rules from data sets. Following the three steps of a knowledge extraction process, namely preprocessing (feature selection), rule discovery process, and postprocessing (rule refinement), the research attempts to address some current difficulties in these three steps and introduces and integrates a °market trading' technique with existing techniques from the field of knowledge discovery and refinement with respect to data mining. In connection with the preprocessing, a feature selection approach that employs neural networks is presented, and three associated pruning schemes that make automatic selection of the pruning threshold are proposed. The proposed neural network techniques are evaluated and compared with the x2statisticbased discretization algorithm, called Chi2, by experimenting with six practical applications. The Chi2 algorithm is investigated as a technique for solving problems in intelligent spatial information systems and fuzzy systems. The case studies show that the Chi2based spatial data filtering can successfully reduce the number of spatial data items and the number of features, and therefore neural network computation can be efficiently performed. A novel approach of employing the Chi2 algorithm to select membership functions for fuzzy systems is proposed. In connection with the applications of fuzzy neural networks (FuNN models), three experimental examinations are demonstrated that an automatic selection of the number and widths of the membership functions by the Chi2based membership function selection method can lead to the improvement of the generalization ability of FuNN fuzzy neural networks. In connection with the rule discovery and refinement process, a novel marketbased rule learning (MBRL) system is developed and its capability of evolving and refining rules is investigated. a classifier systeminspired model, it introduces a novel element by importing existing rule sets generated by other rule extraction techniques into the system. This basic change not only makes the RL system begin with preestablished rule sets with a relatively limited complexity, rather than a random set, but also enhances the likelihood of being able to interpret the evolved rules. Moreover, the MBRL system produces various modifications in each of the layers of the structure. With the modifications introduced by the MBRL system, the problems existing in current classifier systems can be solved or lessened. In this research, the MBRL system is proposed as a postprocessing tool to be used with fuzzy neural networks (FuNN models) and the fuzzy neural network rule extraction technique, ReFuNN, in order to provide a general framework for fuzzy inferencebased rule discovery. Similarly, as a postprocessing tool, the MBRL system is also proposed to be used with feedforward neural networks, and the feedforward neural network rule extraction technique, NeuroLinear, in order to improve the quality of extracted rules from feedforward neural networks. The experimental results show that the MBRL system is a potentially useful additional tool that can be used to refine (fuzzy) neural network extracted rules and possibly discover and add some new, better performance rules. As a result, it can lead to improved performance by increasing the accuracy of the rule inference performance and/or improving the comprehensibility of the rules. By illustrating how the MBRL system succeeded in finding solutions for six learning examples from scratch, the MBRL system is shown to have potential as an alternative generic learning technique that can be used to complement, or be used as an alternative to, conventional connectionist models to accomplish complex computational tasks.  en_NZ 
dc.subject  data stores  en_NZ 
dc.subject  data trawling  en_NZ 
dc.subject  neural network techniques  en_NZ 
dc.subject  fuzzy neural networks  en_NZ 
dc.subject  marketbased rule learning  en_NZ 
dc.subject  complex computational tasks  en_NZ 
dc.subject  Data mining  en_NZ 
dc.subject  feedforward neural networks  en_NZ 
dc.subject.lcsh  T Technology (General)  en_NZ 
dc.subject.lcsh  Q Science (General)  en_NZ 
dc.title  Adaptive knowledge discovery techniques for data mining  en_NZ 
dc.type  Thesis  en_NZ 
dc.description.version  Unpublished  en_NZ 
otago.date.accession  20070416  en_NZ 
otago.school  Information Science  en_NZ 
thesis.degree.discipline  Information Science  en_NZ 
thesis.degree.name  Doctor of Philosophy  
thesis.degree.grantor  University of Otago  en_NZ 
thesis.degree.level  Doctoral Theses  en_NZ 
otago.interloan  yes  en_NZ 
otago.openaccess  Abstract Only  
dc.identifier.eprints  551  en_NZ 
otago.school.eprints  Information Science  en_NZ 
dc.description.references  Almuallim, H. & Dietterich, T. (1994) Learning boolean concepts in the presence of man. irrelevant features, .Artificial Intelligence, No. 69, No.12, pp. 279305. Andrews, R., Cable, R., Diederich, J., Geva, S., Golea, M., Hayward, R., HoStuart, C. & Tickle, A. (1995) An evaluation and comparison of techniques for extracting and refining rules from artificial neural networks, Technical Report, Queensland University of Technology. Arthur, W., Holland, J., Lebaron, B., Palmer, R. & Talyer, P. (1996) Asset pricing under endogenous expectations in an artificial stock market, Technical Report, Santa Fe Institute, Atlas, L., Cole, R., Connor, J., EiSharkawi, M., Marks, R., Muthusamy, Y. & Barnard, E. (1989) Performance comparisons between hackpropagation networks and classification trees on three realworld applications. In Touretzky, D., editor, Advances in Neural Information Processing systems (Volume 2), Morgan Kaufmann, San Mateo, CA. Atonisse, J. (1989) A new interpretation of schema notation that overturns the binary encoding constraint, Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann, pp. 8691. Back, T. & Hoffmeister, F. (1991) Extended selection mechanisms in genetic algorithms, In Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann. Baker, J. (1985) Adaptive selection methods fro genetic algorithms, In Genetic Algorithms and Their Applications: Proceedings ofthe First International Conference on Genetic Algorithms and Their Applications, Erlbaum. Beasley, D., Bull, D. & Martin, R. (1993) An overview of genetic algorithms: part 2, research topics, University Computing, Vol. 15, No. 4, pp. 170181. Almuallim. H. & Dietterich, T. (1994) Learning boolean concepts in the presence of irrelevant features, Artificial Intelligence, No. 69, No.12, pp. 279305. Andrews, R., Cable, R., Diedericli, J., Geva, S., Golea, M., Hayward, R., HoStuart, C. & Tickle, A. (1995) An evaluation and comparison of techniques for extracting and refining rules from artificial neural networks, Technical Report, Queensland University of Technology. Arthur, W., Holland, J., Lebaron, B., Palmer, R. & Talyer, P. (1996) Asset pricing under endogenous expectations in an artificial stock market, Technical Report, Santa Fe Institute. Atlas, L., Cole, R., Connor, J., EiSharkawi, M., Marks, R., Muthusamy, Y. & Barnard, E. (1989) Performance comparisons between backpropagation networks and classification trees on three realworld applications. In Touretzky, D., editor, Advances in Neural Information Processing systems (Volume 2), Morgan Kaufmann, San Mateo, CA. Atonisse, J. (1989) A new interpretation of schema notation that overturns the binary encoding constraint, Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann, pp. 8691. Back, T. & Hoffmeister, F. (1991) Extended selection mechanisms in genetic algorithms, In Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann. Baker, J. (1985) Adaptive selection methods fro genetic algorithms, In Genetic Algorithms and Their Applications: Proceedings ofthe First International Conference on Genetic Algorithms and Their Applications, Erlbaura, Beasley, D., Bull, D. & Martin, R. (1993) An overview of genetic algorithms: part 2, research topics, University Computing, Vol. 15, No. 4, pp. 170181. BenBassat, M. (1982) Pattern recognition and reduction of dimensionality, In Krishriaiah, P. & Karlal L, editors, Handbook of StatisticsIL North Holland, pp. 773791. Benwell, G., Kasabov, N., Purvis, M., Zhang, F., McLennan, B., & Maim S. (1995) Spatial analysis with artificial neural networks, Conference Proceedings of Eighth Australian Joint Artificial Intelligence Conference, Proceedings of the International Workshop on Artificial intelligence and the Environment, Australian Defence Force Academy, Canberra, Australia, pp. 4352. Bilgic, T. & Turksen, I. (1997) Measurement of membership functions: theoretical and empirical work, In Dubois, D. & Prade, H., editors, Handbook of fuzzy Sets and Systems Vol.1 Fundamentals of Fuzzy Sets, Kluwer Academic Publishers, pp. 195232. Bonelli, P., Parodi, A. Sen, S. & Wilson, S. (1990) NEWBOOLE: a fast GBML system, Proceedings of International Conference on Machine Learning, Morgan Kaufmann, San Mateo, California, pp. 153159. Booker, L. (1982) Intelligent behavior as an adaptation to the task environment, PhD Dissertation, University of Michigan. Booker, L. (1985) Improving the performance of genetic algorithms in classifier systems, Proceedings of the First International Conference on Genetic Algorithms and Their Applications, pp. 8092. Booker, L. (1987) Improving search in genetic algorithms, In Davis, L., editor, Genetic Algorithms and Simulated Annealing, Pitman, pp. 6173. Bradshaw, C. J. A., Davis, L. S., Purvis. M., Zhou, Q. & Benwell, G. (2002) Using artificial neural networks to model the suitability of coastline for breeding by New Zealand fur seals, Ecological Modelling, Vol. 148 (2), pp. 111131. Bradshaw, C. J. A., Purvis, M., Raykov, R., Zhou, Q. & Davis, L. S. (2001) Predicting patterns in spatial ecology using neural networks: modelling colonisation by New Zealand fur seals, In R. Denzer, D.A. Swayne, M. Purvis, & G. Schimak, editors, Environmental Software Systems, Environmental Information and Decision Support, Kluwer Academic Publishers, Dordrecht, Netherlands. Vol. 167, pp. 57  65. Breim. , L., Friedman, J., Olsh.en, R. & Stone, C. (1984) Classification and Regression Trees, Wadsworth Intl, Belmont, California. Buckley, J. (1993) Sugeno type controllers are universal controllers, Fuzzy Sets and Systems, Vol. 53, No. 1, pp. 2731. Buckley, J. & Hayashi, Y. (1993) Numerical relationship between neural networks, continuous functions, and fuzzy systems, Fuzzy Sets and Systems, Vol. 60, No. 1, pp. 18. Buckley, J., Hayashi, Y. & Czogala, E. (1993) On the equivalence of neural nets and fuzzy expert systems, Fuzzy Sets and Systems, Vol. 53, No, 2, pp. 129134. Bull, L. (1999) On using ZCS in a simulated continuous doubleauction market, Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufman, San Francisco, CA, pp. 8390. Carpenter, G., Grossberg, S., Markuzon, J. (1992) Fuzzy ARTMAP' a neural network architecture for incremental supervised learning of analog multidimensional maps, IEEE Transactions on Neural Networks, Vol. 3, No. 5, pp. 698713. Chameau, J. & S antamarina, J. ( 1987) M embership functions p art I: comparing m ethod 0 f measurement, International Journal of Approximate Reasoning, vol. 1, pp. 287301. Civan , M. & Trussell H. (1986) Constructing membership functions using statistical data, Fuzzy Sets and Systems, Vol, 18, No. 1, pp. 113. Clearwater, S. (1996) Marketbased Control: A Paradigm for Distributed Resource Allocation, World Scientific Publishing, Singapore. Craven, M. (1996) Extracting Comprehensible models from trained neural network, PhD thesis, University of Wisconsin, Madison. Craven, M. & Shavlik, J. (1994) Using sampling and queries to extract rules from trained neural networks, Proceedings of the Eleventh International Conference on Machine Learning, Morgan Kaufmann, San Mateo, CA. Craven, M. & Shavlik, J. (1999) Rule extraction: where do we go from here? University of Wisconsin Machine Learning Research Group Working Paper 991. Cybenko, G. (1989) Approximation by superpositions of a sigmoidal function, Mathematics of Control, Signals, and Systems, Vol. 2, pp. 303314. Darwin, C. (1897) The Origin of Species by Means of Natural Selection, D. Appleton and Company, New York. Davis, L. (1989) Adapting operator probabilities in genetic algorithms, Proceedings ofthe Fourth International Conference on Genetic Algorithms, Morgan Kaufinann, pp. 6169. Davis, L. (1991) Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York. de Boer, B. (1994) Classifier systems: a useful approach to machine learning?, Masters thesis, Leiden University. De Jong, K. (1975) An analysis of the behaviour of a class of genetic adaptive systems, PhD Thesis, University of Michigan, Arm Arbor. De Jong,K. (1988) Using genetic algorithms to learn task programs: the Pitt Approach, Machine Learning, Vol. 3, pp. 23. Deb, K. & Goldberg, D. (1989) An investigation of niche and species formation in genetic function optimization, In Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann. De Jong, K., Spears, W. & Gordon, D. (1990) Using genetic algorithms for supervised concept learning, Proceedings of IEEE Conference on Tools for Artificial Intelligence TAI ? 0, Vol. 1, pp. 335341. de la Maza, M. & Tidor, B. (1993) An analysis of selection procedures with particular attention paid to proportional and Boltzmann selection, In Proceedings of the Fifth International Conference on Genetic Algorithms, Morgan Kaufmann, Dennis, J. & Schnabel, R. (1983) Numeric Methods for Unconstrained Optimization and Nonlinear Equations, Prentice Halls, Englewood Cliffs, NJ. Devi, B. & Samia, V. (1985) Estimation of fuzzy memberships from histograms, Information Science, Vol. 35, No. 1, pp. 4559. Domingo, C., Gavalda, R. & Watanabe, 0. (2002) Adaptive sampling methods for scaling up knowledge discovery algorithms, Data Mining and Knowledge Discovery, Vol. 6, pp.131152. Dorf, R. (1983) Modern Control Systems, 3rd Edition, AddisonWesley, Reading, MA. Dorigo, M. & Colombetti, M. (1998) Robot Shaping: An Experiment in Behavior Engineering, MIT Press/Bradford Books. Dorigo, M. & Sirtori, E. (1991) Alecsys: a parallel laboratory for learning classifier systems, In Proceedings 0f Fourth International Confrence on Genetic Algorithms, San Diego  California, Morgan Kaufmann, San Mateo, CA. Dowdy, S. & Wearden, S. (1991) St atistics for Research, John Wiley & Sons, Inc, 2nd edition. Drumm, D ., Purvis, M., & Zhou, Q. (1999) Spatial ecology and artificial neural networks: modeling the habitat preference of the sea cucumber (Holothuria leucospiota) on Rarotonga, Cook Islands, Proceedings of SIR C99  The 11 1/ Annual Colloquium of the Spatial Information Research Centre, University of Otago, Dunedin, New Zealand, pp. 141149. Dubois, D. & Prade, H. (1985) A review of fuzzy set aggregation connectives, Information Sciences, Vol. 36, No. 12, pp. 85121. Dubois, D. & Prade, H. (1986) Fuzzy sets and statistical data, European Journal of Operational Research, Vol. 25, No. 3, pp. 345356. Dyckhoff, H. & Pedrycz, W. (1984) Generalized means as a model of compensative connectives, Fuzzy Sets and Systems, Vol. 14, No. 2, pp. 143154. Eshelman, L. (1991) The CHC adaptive search algorithm: how to have safe search when engaging in nontraditional genetic recombination, In Rawlins, G., editor, Foundations of Genetic Algorithms, Morgan Kaufmann, Esposito, F., Malerba, & Semeraro, G. (1997) A comparative analysis of methods for pruning decision trees, IEEE Transactions on Pattern Analysis and Machine intelligence, Vol. 19, No. 5, pp. 476  491. Filev, D. (1991) Fuzzy modeling of complex systems, International Journal of Approximate Reasoning, Vol. 5, No. 3, pp. 281290. Filev, D. (1992) System approach to dynamic fuzzy models, International Journal of General Systems, Vol. 21, No. 3, pp. 311337. Fisher, D. & Mckusick, F. (1989) An empirical comparison of ID3 and backpropagation, In Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Detroit, MI, Morgan Kaufmann, pp. 788793. Fisher, R. (1936) The use of multiple measurements in taxnomic problems, Annual Eugenics, Vol. 7, Part II,pp. 179188. Fogarty, T. (1989) Varying the probability of mutation in the genetic algorithm, Proceedings o the Third International Conference on Genetic Algorithms, Morgan Kaufmann, pp. 104109. Forrest, S. (1990) Implementing semantic network structures using the classifier system, Proceedings ofthe First International Conference on Genetic Algorithms and Their Applications, pp. 2444. Fu, M. (1994) Rule generation from neural networks, IEEE Transactions on Systems, Man and Cybernetics, Vol. 28, No. 8, pp. 11141124. German, G., West, G. & Gahegan, M. (1999) Statistical and AI techniques in GIS classification: a comparison, In Proceedings of SIRC99  The 1 P' Annual Colloquium of the Spatial Information Research Centre, University of Otago, Dunedin, New Zealand. Goldberg, D. (1983) Computeraided gas pipeline operation using genetic algorithms and rule learning, PhD Dissertation, University of Michigan. Goldberg, D. (1989a) Genetic Algorithms in Search, Optimization and Machine Learning, University of Alabama: AddisonWesley Publishing Company, Inc, Reading, MA. Goldberg, D. (1989b) Zen and the art of genetic algorithms, Proceedings of the Third International Conference on Genetic Algorithm, Morgan Kaufinarm., pp. 8085. Goldberg, D. (1989c) Sizing populations for serial and parallel genetic algorithms, In Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann. Goldberg, D. & Deb, K. (1991) A comparative analysis of selection schemes used in genetic algorithms, In Rawlins, G., editor, Foundations of Genetic Algorithms, Morgan Kaufmann. Goldberg. D. & Richardson, J. (1987) Genetic algorithms with sharing for multimodal function optimization, In Grefenstette, J., editor, Genetic Algorithms and Their Applications: Proceedings of the Second International Conference on Genetic Algorithms, Erlbaurn, Grefenstette, J. (1986) Optimization of control parameters for genetic algorithms, IEEE Transactions on System, Man, and Cybernetics, Vol. 16, No. 1, pp. 122128. Grefenstette, J. (1988) Credit assignment in rule discovery systems based on genetic algorithms, Machine Learning, Vol. 3, pp. 225245. Grossberg, S. (1976) Adaptive pattern classification and universal recording: I. Parallel development and coding ofneural feature detectors, Biological Cybernetics, Vol. 23, pp. 121134. Gupta, M. & Qi, J. (1992) On fuzzy neuron models, In Zadeh, L. & Kacprzyk, J. editors, Fuzzy Logic for the Management of Uncertainty, John Wiley, New York, pp. 479491. Hales, S., Zhou, Q., Lewis, S. & Purvis, M. (1998a) Connectionist modelling of asthma incidence in New Zealand, Proceedings of the 10th Colloquium of the Spatial Information Research Centre, University of Otago, New Zealand, pp. 111117. Hales, S., Zhou, Q., Lewis, S. & Purvis, M. (1998b) Connectionist modelling of asthma incidence in New Zealand, Information Science Discussion Paper Series, Number 98/10, ISSN 11726024, University of Otago, Dunedin, New Zealand, Hanson, S. & Pratt, L. (1989) Comparing biases for minimal network construction with backpropagation, In Touretzky, D., editor, Advances in Neural Information Processing, Vol. 1, pp. 177185. Hancock, P. (1994) An empirical comparison of selection methods in evolutionary algorithms, In Fogarty, T., editor, Evolutionary Computing: AISB Workshop, Leeds, U.K., SpringerVerlag. Hassibi, B. & Stork, D. (1993) Second order derivatives for network pruning: optimal brain surgeon, Neural Information Processing Systems, Vol. 5, pp. 164171. Hayashi Y., Buckley J. & Czogala, E. (1993) Fuzzy neural network with fuzzy signals and weights, International Journal of Intelligent Systems, Vol. 8, No. 4, pp. 527537. Haykin, S. (1999) Neural Networks: A Comprehensive Foundation, Prentice Hall, Upper Saddel River, New Jersey, pp. 234245. Hebb, D. (1949) The Organization of Behavior: A Neuropsychological Theory, Wiley, New York. Hersh, H. & Carmazza (1976) A fuzzy set approach to modifiers and vagueness in natural language, Journal of Experimental Psychology: General, Vol. 105, No. 3, pp. 254276. Hertz, J., Krogh, A. & Palmer, R. (1991) Introduction to the Theory of Neural Computation, AddisonWesley, Reading, Mass. Hillis, W. (1992) Coevolving parasites improve simulated evolution as an optimization procedure, In Langton, C., Taylor, C., Farmer, J. & Rasmussen, S., editors, Artificial Life II, AddisonWesley. Hinton, G. (1989) Conn.ectionist learning procedure, Artificial Intelligence, Vol. 40, pp. 185234. Holland, J. (1975) Adaptation in Natural an Artificial Systems, University of Michigan Press. Holland, J. (1986) Escaping Brittleness: the possibilities of generalpurpose learning algorithms applied to parallel rulebased systems, In Michalski, R., Carbonell, J. & Mitchell, T., editors, Machine Learning: An Artificial Intelligence Approach, Vol. 2, Morgan Kaufmann, pp. 593623. Holland, J. & Reitman, J. (1978) Cognitive systems based on adaptive algorithms, In Waterman, D. & HayessRoth, F., editors, Patterndirected inference Systems. Academic Press, New York. Holmblad, L. & Ostergaard, J. (1982) Control of a cement kiln by fuzzy logic, In Gupta, M. & Sanchez, E., editors, Fuzzy Information and Decision Processes, NorthHolland, New York, pp. 389399. Holmes, J. (1996) Evolutionassisted discovery of sentinel features in Epidemiologic Surveillance, PhD Thesis, Drexel University. Hop field, J. (1982) Neural networks and physical systems with emergent collective computational abilities, Proceedings of the National Academy of Science, USA, Vol. 81, pp. 25542558. FIorikawa, S Furubashi, T. & Uchikawa, Y. (1992) Fuzzy modelling using fuzzy neural networks with the backpropagation algorithm, IEEE Transactions on Neural Networks, Vol 3, No. 5, pp. 801806. Janikow, C. & Michalewicz, Z. (1991) An experimental comparison of binary and floating point representations in genetic algorithms, Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann. Ji, C., Snapp, R. & Psaltis, D. (1990) Generalizing smoothness constraints from discrete samples, Neural Computation, Vol. 2, No. 2, pp. 188197. Kasabov, N. (1993a) Learning fuzzy production rules for approximate reasoning with connectionist production systems, Proceedings of the International Conference on Artificial Neural Networks. New York, SpringerVerlag, pp. 337345. Kasabov, (1993b) Learning fuzzy rules and membership functions in fuzzy neural networks, Proceeding of ANNES? 3, Dunedin, New Zealand. Kasabov, N. (1996) Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering. The MIT Press, Cambridge, MA. Kasabov, N., Kim, J., Watts, M. & Gray, A. (1997) FuNN – A fuzzy neural network architecture for adaptive learning and knowledge acquisition, Information Science, 101 (34), pp. 155175. Keller, J. & Hunt, D. (1985) Incorporating fuzzy membership functions into the perceptron algorithm, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 7, No. 6, pp. 693699. Kerber, R. (1992) ChiMerge: di scretization of numeric attributes, Proceedings ofNinth National Conference on Artificial Intelligence, AAAI Press/The MIT Press, pp. 123128. Kira, K. & Rendell, L. (1992) The feature selection problem: traditional methods and a new algorithm, Proceedings of the Tenth National Conference on Artificial Intelligence, AAAI Press/MIT Press, Menlo Park, pp. 129134. Klir, G. & Yuan, B. (1995) Fuzzy Sets and Fuzzy Logic: 'Theory and Applications, Prentice Hall P T R, Upper Saddle River, NJ. Kochen, M. & Badre, A. (1974) On the precision of adjectives which denote fuzzy sets, Journal of Cybernetics, Vol. 4, pp. 4959. Kohonen, T. (1982) Selforganized formation of topologically correct feature maps, Biological Cybernetics, Vol. 43, pp. 5969. Kosko, B. (1992) Fuzzy systems as universal approximators, Proceedings of First IEEE International conference on Fuzzy systems, San Diego, pp. 11531162. Langley, P. (1998) The computeraided discovery of scientific knowledge, In Proceedings of the ls' International Conference on Discovery Science, SpringerVerlag, New York. Lanzi, P., Stolzmann, W., Wilson, S. (2000) Learning Classifier systems: From Foundations to Applications, Vol. 1813 of LNAI, SpringerVerlag, Berlin. Lebaron, B. Arthur, W. & Palmer, R. (2000) The time series properties of an artificial stock market, Journal of Economics Dynamics and Control, Vol. 24, No. 57, pp. 679702. Lee, S. & Lee, E. (1974) Fuzzy sets and neural networks, Journal of Cybernetics, Vol. 4, No. 2, pp. 83103. Legendre, & Legendre, P. (1983) Numerical Ecology, Elsevier Scientific, New York. Liu, H. & Motoda, H. (1998) Feature Selection for Knowledge Discovery and Data Mining, Kluwer Academic Publishers. Liu, H. & Motoda, H. (2002) On issue of instance selection, Data Mining and Knowledge Discovery, Vol. 6, pp. 115130. Liu, H. & Setiono, R. (1995a) Chi2: feature selection and discretization of numeric attributes, Proceedings of the 71/i IEEE International Conference on Tools with Artificial Intelligence, pp. 388391. Liu, H. & Setiono, R. (1995b) Discretization of ordinal attributes and feature selection, Technical Report TRB4/95, Department of Information Systems and Computer Science, National Univesity of Singapore. Liu, H. & Tan, S. (1995) X2R: A fast rule generator, Proceeding of IEEE International Conference on Systems, Man and Cybernetics (IEEE Press), Vancouver, Canada, pp. 631635. Maiers, J. & Sherif, Y. (1985) Applications of fuzzy set theory, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 15, No. 1, pp. 175189. Mandani, E. (1976) Advances in the linguistic synthesis of fuzzy controllers, International Journal of ManMachine Studies, Vol. 8, No. 6, pp. 669678. Mang, G., Lan, H. & Zhang, L. (1995) A geneticbased method of generating fuzzy rules and membership functions by learning from examples, Proceedings of ICONIP? 5, China, Vol. 1, pp. 335338. Matheus, C. (1991) The need for constructive induction, In Birnbaum, L. & Collins, G., editors, Machine Learning  Proceedings of the Eighth International Workshop, pp. 173177. Medin, D., Wattenmaker, W. & Michalski, R. (1987) Constraints and preference in inductive learning: an experimental study of human and machine performance, Cognitive Science, Vol. 11, pp. 299339. McCulloch, W. & Pitts, W. (1943) A logical calculus of the ideas immanent in nervous activity, Bulletin of Mathematical Biophysics, Vol. 5, pp. 115133. Mendenhall W. & Sincich, T. (1995) Statistics for Engineering and the Science, zith edition, Prentice FlailInternational. Mettler, E., Gregg, T. & Schaffer, H. (1988) Population Genetics and Evolution, 2nd on, Prentice Hall, Englewood Cliffs, NJ. Michalski, R. (1986) Understanding the nature of learning: issues and research directions, In Michalski, R., Carbonell, G. & Mitchell, M., editors, Machine Learning; An Artificial Intelligence Approach (volume II), Morgan Kaufinann, Los Altos, CA. Michie, D. (1974) On Machine Intelligence, Edinburgh University Press. Migrin, A. (1993) Neural Networks for Pattern Recognition, The MIT Press, Cambridge, MA. Miller, M., Krieger, D., FIardy, N., Hibbert, C. & Tribble, E. (1994) An automated auction in ATM network bandwidth, Sun Labs Technical Report SML940508. Mingers, J. (1987) Expert systems  rule induction with statistical data, Journal of Operational Research Society, Vol. 38, pp. 3947. Minsky, M. (1963) Steps toward artificial intelligence, Computers and Thought, Feigenbaum, E. & Feldman, J editors, McGrawHill, New York, NY. Minsky, M. & Papert, S. (1969) Perceptrons, MIT Press, Cambridge. Mitchell, M. (1996) An Introduction to Genetic Algorithms, A Bradford Book, The MIT Press, Cambridge, MA. Mitchell, T. (1999) Machine learning and data mining, Communications of The ACM, Vol. 42, No. 11, pp. 3036. Mitlohner, S. (1996) Classifier systems and economic modelling, Proceedings of the APL 96 Conference on Designing the Future, Vol, 26, No. 4, pp. 786. Modrzejewski, M. (1993) Feature selection using rough sets theory, Proceedings ofthe European Conference on Machine Learning, Vienna, Austria, pp. 213226. Montana, D. & Davis, L.. (1989) Training feedforward networks using genetic algorithms, In Proceedings of the International Joint Conference on Artificial Intelligence, Morgan Kaufmann. Neisser, & Weene, P. (1962) Hierarchies in concept attainment, Journal of Experimental Psychology, Vol. 64, pp. 640645. Niblett, T. & Bratko, L (1986) Learning decision rules in noisy domains, Proceedings of Expert Systems 86, Cambridge University Press, Cambridge. Nit, P. (1986) Blackboard systems: the blackboard model of problemsolving and the evolution of blackboard architecture, AI Magazine, Vol. 7, No. 2, pp. 3853. Norwich, a. & Turksen, I. (1982) The construction of membership functions, In Yager, R., editor, Fuzzy Sets and Possibility Theory: Recent Developments, Pergamon Press, New York, pp. 6167. Packard, N.H. (1990) A genetic learning algorithm for the analysis of complex data, Complex Systems, Vol. 4, No. 5, pp. 543572. Pagallo, G. & Haussler, D. (1990) Boolean feature discovery in empirical learning, Machine Learning, Vol. 5, pp. 7199. Pal, S. & Mitra, S. (1992) Multilayer perceptron, fuzzy sets, and classification, IEEE Transactions on Neural Networks, Vol. 3, No. 5, pp. 683697. Pawlak„ Z. (1991) Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers. Pedrycz, W. (1991) Neurocomputations in relational systems, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 13, No, 3, pp. 289297. Pinker, S. (1979) Formal models of language learning, Cognition, Vol. 7, pp. 217283. Pod, E. (1998) Visualizing neural networks, PhD Thesis, University of Stellenbosch, South Africa. Primeaux, D. (2001) Partially opening the black box. an ANN with inspectable, hidden layers, ACTS International Journal of Computer and Information Science, Vol.2, No, 2, pp. 67 73. Purvis, M., Kasabov, N., Benwell, G., Zhou, Q. & Zhang, F. (1999) Neurofuzzy methods for environmental modelling, International Journal of Systems Research and Information Systems, Vol 8, pp. 221239. Purvis, M., Kasabov, N., Zhang, F., & Benwell, G. (1996) Connectionistbased methods for knowledge acquisition from spatial data, Proceedings of the TASTED International Conference, L4STEDACTA Press, Anaheim, CA, pp. 151154. Purvis, M,, Zhou. Q., Cranefield, S., Ward, R., Raykov, R. & Jessberger, D. (2001) Spatial information modelling and analysis in a distributed environment, Environmental Modelling and Software, Vol. 16, No. 5, pp. 439445. Quinlan, J. (1986) Induction of decision trees, Machine Learning, Vol. 1, No. 1, pp. 81106., Quinlan, J. (1987) Simplifying decision trees, International Journal of ManMachine Studies, Vol. 27, pp. 221234. Quinlan, J. 993 C4.5: Programs br Machine Learning, ad:maim. Reed, R. (1993) Pruning algorithms  a survey, IEEE Transactions on Neural Networks', Vol. 4, No. 5, pp. 740747. Richards, R. (1995) Zerothorder shape optimization utilizing a learning classifier system, PhD Dissertation, Mechanical Engineering Department, Stanford University. Rinnooy Kan, C. & Timmer, T. (1989) Global optimization: A survey, International Series of Numerical Mathematics. Vol. 87, Birkhauser Verlag, Basel. Roberts, G. (1993) Dynamic planning for Classifier Systems, Proceedings of the Fifth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, pp. 231237. Robertson, G. & Riolo, R. (1988) A tale of two classifier systems, Machine Learning, Vol. 3, pp.139159. Rosenblatt, F. (1958) The perceptron: a probabilistic model for information storage and organization in the brain, Psychological Review, Vol. 65, pp.386408. Ross, T., Noviskey, M., Axtell, M., Gadd, D. & Goldman, J. (1994) Pattern theoretic feature extraction and construction induction, In Proceedings of CMML/COLT? 4 Workshop on Constructive Induction and Change of Representation. Roubos, H. & Setnes, M. (2002) Compact fuzzy models and classifiers through model reduction and evolutionary optimization, In Chambers, L., editor, The Practical Handbook of Genetic Algorithms Applications, Chapman & Hall/CRC, 2nd edition. Ruan, D. (1997) Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms, Kluwer Academic Publishers, Norwell, MA. Rumelhart, D., Hinton, G. & Williams, R. (1986). Learning internal representations by error propagation, Ln Rumelhart, D. & McClelland, J., editors, Parallel Distributed Processing: Explorations in the microstructure of cognition. Volume 1: Foundations. MIT Press, Cambridge, MA. Ryan, T. (1997) Modern Regression Methods, Wiley, New York. Sachs, L. (1984) Applied Statistics: A Handbook of Techniques, SpringerVerlag, New York. Saxon, S. & Barry, A. (2000) XCS and the Monk's problems, in Lanzi, S. & Wilson, S., editors, Learning Classier Systems: From Foundations to Applications, Vol. 1813 of LNAI, Springer Verlag, Berlin, pp. 223242. Schaffer, J, Caruana, R., Eshelman, L. & Das, R. (1989) A study of control parameters affecting online performance of genetic algorithms for function optimization, In Proceedings of the Third International Conference on Genetic Algorithms, Morgan Kaufmann, Schoenenburg, E. (1990) Stock price prediction using neural networks: a project report, Neurocomputing, Vol. 2, pp. 1727. Schulenburg, S. & Ross, P. (1999) An adaptive agent based economic model, Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufman, San Francisco, CA, pp. 265284. Setiono, R. (1996) Extracting rules from neural networks by pruning and hiddenunit splitting, Technical Report, National University of Singapore. Setiono, R. (1997) A penaltyfunction approach for pruning feedforward neural networks, Neural Computation, Vol. 9, No. 1, pp. 185204. Setiono, R. & Liu, H. (1995) Extracting rules from neural networks by pruning and hiddenunit splitting, Technical Report, DISCS, National University of Singapore. Setiono, R. & Liu, H. (1997a) Neural network feature selectors, IEEE Trans. on Neural Networks, Vol.8, No. 3, pp. 654662. Setiono, R.& Liu, H. (1997b) NeuroLinear: from neural networks to oblique decision rules, Neurocomputing, Elsevier, Vol. 17, No.1, pp. 125. Shannon, C. (1948) A mathematical theory of communication, Bell System Technical Journal, Vol. 27, pp. 379423 and pp. 623656. Shavlik, J., Mooney, R. & Towell, G. (1991) Symbolic and neural net learning algorithms: an empirical comparison, Machine Learning, Vol. 6, pp.111143. Shigeo, A. & Lan, M. (1995) A method for fuzzy rules extraction directly from numerical data and its application to pattern classification, IEEE Transactions on Fuzzy Systems, Vol. 3, No.1, pp. 1828. Shustorovich, A. & Thrasher, C. (1996) Neural network positioning and classification of handwritten character, Neural Networks, Vol. 9, No. 4, pp. 685693. Shyllon, E. (2000) Fuzzy system approach: contemporary tools for h ridling uncertainty in geospatial data and GISbased analysis, GIS Science 2000: The First International Conference on Geographic Information Science, Vol. 1, pp. 384385. Simpson, P. (1992) Fuzzy minmax neural networks, Part 1: Classification, IEEE Transactions on Neural Networks, Vol. 3, No. 5, pp. 776786. Smith, S. (1980) A learning system based on genetic algorithm, PhD Thesis, Department of Computer Science, University of Pittsburgh. Sneath, P. Sokol, R., (1973) Numerical Taxonomy, W. H. Freeman, San Francisco, Stolzmann, W. (2000) An Introduction to anticipatory classifier system, In Lanzi, P., Stolzmann, W. & Wilson, S., editors, Learning Classifier Systems: From Foundations to Applications, Vol. 1813 of LNAI, SpringerVerlag, Berlin, pp. 303320. Stone, M. (1974) Crossvalidatory choice and assessment of statistical predictions, Journal of/he Royal Statistical Society, Vol. 36, pp.111147. Sugeno, M. & Park, G. (1993) An approach to linguistic instruction based learning, International Journal of Uncertainty, Fuzziness and Knowledgebased Systems, Vol. 1, No. 1, pp. 1956. Sugeno, M. & Yasukawa, T. (1993) A fuzzy logicbased approach to qualitative modeling, IEEE Transactions on Fuzzy systems, Vol. 1, No. 1, pp. 731. Takagi, H. & Hayashi, I. (1991) NNdriven fuzzy reasoning, Internal Journal of Approximate Reasoning, Vol. 5, No. 3, pp. 191213. Thrun, S. (1995) Extracting rules from artificial neural networks with distributed representations, In Advances in Neural Information Processing Systems, Vol. 7, The MIT Press, Cambridge, MA. Toblers, W. (1969) Geographical filters and their inverses, Geographical Analysis, Vol. 1, pp. 234253. Towel', G. & Shavlik, J. (1993) The extraction of refined rules from knowledge based neural networks, Machine Learning, Vol. 131, pp. 71101. Turksen, L (1988) Stochasic fuzzy sets: a survey, Combing Fuzzy Imprecision with Probabilistic Uncertainty in Decision Making, SpringerVerlag, New York, pp. 168183. Turksen, 1. (1991) Measurement of membership functions and their asE.: ',merit, Fuzzy Sets and systems, vol. 40, pp. 538. UCI ( 1998) U CI M achine L earning Repository, University o f C alifornia at Irvine M achine Learning Group. hrip://www.ics.uci.edu/–mlearril.MLRepository.html. von der Malsburg, C. (1973) Selforganizing of orientation sensitive cells in the striate cortex, Kybernetik, Vol. 14, pp. 85100. Wang , M., Iyer, B. & Vitter, J. (1998) MIND: a scalable mining for classifier in relational databases, In Proceedings of the ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery. Waterman, D. (1970) Generalization learning techniques for automating the learning o f heuristics, Artificial Intelligence, Vol. 1, pp. 121170. Watrous, R. (1987) Learning algorithms for connectionist networks: applied gradient methods of nonlinear optimization, Proceedings IEEE First International Conference on Neural Networks, IEEE Press, New York, pp. 619627. Weigend, A., Rumelhart, D. & Huberman, B. (1988) Backpropagation, weight elimination ans time series prediction, Proceedings of the 1988 Connectionist Models Summer School, Morgan Kaufmann, San Mateo, CA, pp. 105116. Weigend, A., Rumelhart, D. & Hubernian, B. (1991) Generalization by weightelimination with application to forecasting, In Lippmann, R., Moody, J. & Touretzky, D., editors, Advances in Neural Information Processing, Vol. 3, pp. 875882. • Weiss, M. & 'Caponleas, L (1989) An empirical comparison of pattern recognition, neural nets, an.d machine learning classification methods, in Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, Detroit, MI. Morgan Kaufmann, pp. 688693. Wiener, N. (1948), Cybernetics, The MIT Press and Wiley, New York. Wikel, J. Dow, E. & Heathman, M. (1996) Interpretative neural network for QSAR, NetSci, Accessed on 19 November 2002, Journal Online. http://www.netsci.org,/Science/Compchem/feature02.htrn1 Wilson, S. (1985) Knowledge growth in an artificial animal, Proceedings of the First International Conference on Genetic Algorithms and Their Applications, Lawrence Eribaum, Hillsdale, New Jersey, pp. 1623. Wilson, S. (1987) Classifier systems and the animat problem, Machine Learning, Vol. 2, pp. 199 228. Wilson, S. (1994) ZCS: a zero level classifier system, Evolutionary Computation, Vol. 2, No. 1, pp. 118. Wilson, S. (1995) Classifier fitness based on accuracy, Evolutionary computation, Vol. 3, No. 2, pp. 149175. Wilson, S. (1999) Get real! XCS with continuousvalued inputs, In Booker, L., Forrest, S., Mitchell, M. & Riolo, R., editors, Festschrift in Honor of John H. Holland, Center for the Study og complex Systems, pp. 111121. Wilson, S. (2000) Mining oblique data with XCS, I11iGAL Report No. 2000028, Illinois Genetic Algorithms Laboratory, University of Illinois at UrbanaChampaign. Wilson, S. & Goldberg, D. (1989) A critical review of classifier systems, In Proceedings of the Third International Conference on genetic Algorithms, Morgan Kaufman. Wnek, J. & Michalski, R. (1994) Hypothesisdriven constructive induction in AQ17HCI: a method and experiments, Machine Learning, Vol. 14, pp. 139168. Wright, A. (1991) Genetic algorithms for real parameter optimization, In G. Rawlins, editor, Foudations of Genetic Algorithms. Morgan Kaufmann. Wyse, N., Dubes, R. & Jain, A. (1980) A critical evaluation of intrinsic dimensionality algorithms, In Gelsema, E. & Kanal, L., editors, Pattern Recognition in Practice, Morgan Kaufmann, pp. 415425. Yamakawa, T. & Furukawa, M. (1992) A design algorithm of membership functions for a fuzzy neuron using examplebased learning, Proceedings of the First IEEE Conference on fuzzy systems, pp. 7582. Yager, R. (1992) Implementing fuzzy logic controllers using a neural network framework, Fuzzy Sets and Systems, Vol. 48, No. 1, pp. 5364. Zadeh, L. (1965) Fuzzy sets and systems, In Fox, J. editor, System Theory, Polytechnic Press, Brooklyn, New York, pp. 338353. Zadeh, L. (1971) Towards a theory of fuzzy systems, In Kalman, R. & De Clairis, R., editors, Aspects of Networks and Systems Theory, Holt, Rinehart & Winson, New York, pp. 469490. Zadeh, L. (1972) A rational for fuzzy control Journal of Dynamical Systems, Measurement, and Control, Vol. 94, No. 1, pp. 34. Zadeh, L. (1973) Outline of a new approach to the analysis of complex systems and decision processes,, IEEE Transactions on Systems, Man and Cybernetics, vol 1, No 1 pp 2844. Zadel, L (1974) A new approach to analysis, In Marois, M. ,a editor ,Man and Computer North Holland, New York, pp 5594 Zhang, H., Ma, X., Xu, W. & Wang, P. (1993) Design fuzzy controllers for complex systems with an application to 3stage inverted pendulum, Information Sciences, Vol. 72, No. 3, pp. 271284. Zhou, Q., Drumm, D. & Purvis, M. (2001) Adaptive knowledge discovery techniques for identifying the habitat preference of the sea cucumber, Holotharia leucospilota on Rarotonga, Cook Islands, a book chapter to appear in Ecological Informatic. Zhou, Q. & Purvis, M. (1999) Knowledge extraction using marketbased rule evolution, Proceedings of ICONIP/ANZIIS/ANNES? 9 International Workshop, Dunedin, New Zealand, pp. 203206. Zhou, Q., Purvis, M., & Kasabov, N. (1997a) A membership function selection method for fuzzy neural networks, Proceedings of the International Conference on Neural Information Processing and Intelligent Systems, Springer, Singapore, pp. 785788. Zhou, Q., Purvis, M., & Kasabov, N. (1997b) A membership function selection method for fuzzy neural networks, Information Science Discussion Paper Series, Number 97/15, ISSN 11726024, University of Otago, Dunedin, New Zealand. Zhuang, X. & Engel, B. (1990) Classification of multispectral remote sensing data using neural network vs. statistical methods, In Proceedings of the International Winter Meeting of the American Society of Agricultural Engineers,  en_NZ 
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