A Genetic Algorithm-Based Approach for Building Accurate Decision Trees

References

• Banzhaf W., Nordin P., Keller R., Francone F.Genetic Programming: An Introduction (1998) (Morgan Kaufmann, San Francisco, CA) Crossref, Google Scholar
• Bauer E., Kohavi R. An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Machine Learning (1999) 36:105–139Crossref, Google Scholar
• Blake C., Merz C. UCI repository of machine learning databases. (1998) . Department of Information and Computer Science, University of California, Irvine, CA http://www.ics.uci.edu/~mlearn/MLRepository.htmlGoogle Scholar
• Bojarczuk C., Lopes H., Freitas A. Genetic programming for knowledge discovery in chest pain diagnosis. IEEE Engineering in Medicine and Biology (2000) 19:38–44Crossref, Google Scholar
• Bot M., Langdon W., Poli R., Banzhaf W., Langdon W., Miller J., Nordin P., Fogarty T. Application of genetic programming to induction of linear classification trees. Proceedings of the Third European Conference on Genetic Programming (2000) (Springer- Verlag, Edinburgh, Scotland, U.K.) 247–258Crossref, Google Scholar
• Breiman L. Bagging predictors. Machine Learning (1996) 24:123–140Crossref, Google Scholar
• Breiman L. Arcing classifiers. Annals of Statistics (1998) 26:801–849Crossref, Google Scholar
• Breiman L., Friedman J. H., Olshen R. A., Stone C. J.Classification and Regression Trees (1984) (Wadsworth and Brooks/Cole, Monterey, CA) Google Scholar
• Cormen T., Leiserson C., Rivest R.Introduction to Algorithms (1990) (MIT Press, Cambridge, MA) Google Scholar
• Deo N.Graph Theory with Applications to Engineering and Computer Science (1974) (Prentice-Hall, Englewood Cliffs, NJ) Google Scholar
• Dhar V., Chou D., Provost F. Discovering interesting patterns for investment decision making with GLOWER—a genetic learner overlaid with entropy reduction. Data Mining and Knowledge Discovery (2000) 4:251–280Crossref, Google Scholar
• Fayyad U., Piatetsky-Shapiro G., Smyth P., Uthurusamy R.Advances in Knowledge Discovery and Data Mining (1996) (MIT Press, Cambridge, MA) Google Scholar
• Folino G., Pizzuti C., Spezzano G., Poli R., Banzhaf W., Langdon W., Miller J., Nordin P., Fogarty T. Genetic programming and simulated annealing: a hybrid method to evolve decision trees. Proceedings of the Third European Conference on Genetic Programming (2000) Springer-Verlag, Edinburgh, Scotland, U.K.:294–303Crossref, Google Scholar
• Freitas A., Klösgen W., Zytkow J. Evolutionary computation. Handbook of Data Mining and Knowledge Discovery (2002a) (Oxford University Press, New York) 698–706Google Scholar
• Freitas A., Ghosh A., Tsutsui S. A survey of evolutionary algorithms for data mining and knowledge discovery. (2002b) (Springer-Verlag, Heidelberg, Germany) . Forthcoming in Advances in Evolutionary ComputingCrossref, Google Scholar
• Freund Y., Schapire R. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences (1997) 55:119–139Crossref, Google Scholar
• Fu Z. Using genetic algorithms to develop intelligent decision trees. (2000) . Ph.D. dissertation, University of Maryland, College Park, MDGoogle Scholar
• Fu Z., Golden B., Lele S., Raghavan S., Wasil E., Laguna M., González-Velarde J. L. Building a high-quality decision tree with a genetic algorithm: a computational study. Computing Tools for Modeling, Optimization and Simulation: Interfaces in Computer Science and Operations Research (2000) (Kluwer Academic Publishers, Boston, MA) 25–38>Crossref, Google Scholar
• Holland J. H.Adaptation in Natural and Artificial Systems (1975) (University of Michigan Press, Ann Arbor, MI) Google Scholar
• Kennedy H., Chinniah C., Bradbeer P., Morss L., Corne D., Shapiro J. The construction and evaluation of decision trees: a comparison of evolutionary and concept learning methods. Evolutionary Computing, Lecture Notes in Computer Science (1997) (Springer-Verlag, Berlin, Germany) 147–161Google Scholar
• Koza J., Schwefel H-P., Maenner R. Concept formation and decision tree induction using the genetic programming paradigm. Parallel Problem Solving from Nature (1991) (Springer-Verlag, Berlin, Germany) 124–128Crossref, Google Scholar
• Koza J.Genetic Programming: On the Programming of Computers by Means of Natural Selection (1992) (MIT Press, Cambridge, MA) Google Scholar
• Marmelstein R., Lamont G., Koza J. Pattern classification using a hybrid genetic program-decision tree approach. Proceedings of the Third Annual Conference on Genetic Programming (1998) (Morgan Kaufmann, San Francisco, CA) Google Scholar
• Menard S.Applied Logistic Regression Analysis (1995) (Sage, Thousand Oaks, CA) Google Scholar
• Michalewicz Z.Genetic Algorithms + Data Structures = Evolution Programs (1996) (Springer-Verlag, New York) Crossref, Google Scholar
• Nikolaev N., Slavov V. Inductive genetic programming with decision trees. Intelligent Data Analysis (1998) 2 http://www-east.elsevier.com/idaCrossref, Google Scholar
• Quinlan J. Induction of decision trees. Machine Learning (1986) 1:81–106Crossref, Google Scholar
• Quinlan J. R.C4.5: Programs for Machine Learning (1993) (Morgan Kaufmann, San Mateo, CA) Google Scholar
• Ripley B. D.Pattern Recognition and Neural Networks (1996) (Cambridge University Press, Cambridge, UK) Crossref, Google Scholar
• Ryan M., Rayward-Smith V. The evolution of decision trees. Proceedings of the Third Annual Conference on Genetic Programming (1998) (Morgan Kaufmann, San Francisco, CA) 350–358Google Scholar