Abstract
Using an evolutionary approach for evolving classifiers can simplify the classification task. It requires no domain knowledge of the data to be classified nor the requirement to decide which attribute to select for partitioning. Our method, called the Genetic Evolved Classifier (GEC), uses a simple structured genetic algorithm to evolve classifiers. Besides being able to evolve rules to classify data in to multi-classes, it also provides a simple way to partition continuous data into discrete intervals, i.e., transform all types of attribute values into enumerable types. Experiment results shows that our approach produces promising results and is comparable to methods like C4.5, Fuzzy-ID3 (F-ID3), and probabilistic models such as modified Naïve-Bayesian classifiers.
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C. L. Blake and C. J. Merz. UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science, 1998.
H. M. Chen and S. Y. Ho, “Designing an Optimal Evolutionary Fuzzy Decision Tree for Data Mining”, Proceedings of the Genetic and Evolutionary Computation Conference, pp. 943–950, 2001.
K. A. De Jong, W. M. Spears, D. F. Gordon, “Using Genetic Algorithms for Concept Learning”, Machine Learning, vol. 13, no. 2, pp. 161–188, 1993.
U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, “From data mining to knowledge discovery: An overview”, Advances in Knowledge Discovery and Data Mining, chap. 1, pp. 1–34, AAAI Press and MIT Press, 1996.
J. H. Holland, Adaptation in Natural and Artificial Systems, Univ. of Michigan Press (Ann Arbor), 1975.
J. H. Holland, “Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems”, Machine Learning, an artificial intelligence approach, 2, 1986.
P. Horton and K. Nakai, “A Probablistic Classification System for Predicting the Cellular Localization Sites of Proteins”, Intelligent Systems in Molecular Biology, pp. 109–115, 1996.
R. Kohavi, “Scaling Up the Accuracy of Naïve-Bayes Classifiers: a Decision-Tree Hybrid”, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 202–207, 1996.
C. H. Liu, C. C. Lu and W. P. Lee, “Document Categorization by Genetic Algorithms”, IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 3868–3872, 2000.
S. F. Smith, A Learning System Based on Genetic Adaptive Algorithms, PhD Thesis, Univ. of Pittsburgh, 1980.
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Hsu, W.W., Hsu, CC. (2002). GEC: An Evolutionary Approach for Evolving Classifiers. In: Chen, MS., Yu, P.S., Liu, B. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2002. Lecture Notes in Computer Science(), vol 2336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47887-6_44
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DOI: https://doi.org/10.1007/3-540-47887-6_44
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