A Dynamic Programming Based Pruning Method for Decision Trees

References

• Bellman R.Dynamic Programming (1957) (Princeton University Press, Princeton, NJ) Google Scholar
• Blake C., Keogh E., Merz C. J.UCI Repository of Machine Learning Databases (1998) (Dept. of Information and Computer Science, University of California, Irvine, CA) . http://www.ics.uci.edu/~mlearn/MLRepository.htmlGoogle Scholar
• Bohanec M., Bratko I. Trading accuracy for simplicity in decision trees. Machine Learning (1994) 15:223–250Crossref, Google Scholar
• Bradley P. S., Fayyad U. M., Mangasarian O. L. Mathematical programming for data mining: formulations and challenges. INFORMS J. Computing (1999) 11:217–238Link, Google Scholar
• Brazdil P., Gama J.Evaluation/characterization of classification algorithms (1994) (LIACC, University of Porto, Rua Campo Alegre 823 4150 Porto, Portugal) . http://www.ncc.up.pt/liacc/ML/statlog/index.htmlGoogle Scholar
• Breiman L. Bagging predictors. Machine Learning (1996) 24:123–140Crossref, Google Scholar
• Breiman L., Friedman J. H., Olshen R. A., Stone C. J.Classification and Regression Trees (1984) (Wadsworth, Belmont, CA) Google Scholar
• Clark L. A., Pregibon D., Chambers J. M., Hastie T. J. Chapter 9: Tree-based models. Statistical Models in (1992) (S. Wadsworth and Brooks, Pacific Grove, CA) 377–419Google Scholar
• Crawford S. L. Extensions to the CART algorithm. International J. Man-Machine Studies (1989) 31:197–217Crossref, Google Scholar
• Detrano R., Janosi A., Steinbrunn W., Pfisterer M., Schmid J., Sandhu S., Guppy K., Lee S., Froelicher V. International application of a new probability algorithm for the diagnosis of coronary artery disease. American J. Cardiology (1989) 64:304–310Crossref, Google Scholar
• Efron B. Estimating the error rate of a prediction rule: improvements on cross-validation. J. American Statistical Association (1983) 78:316–331Crossref, Google Scholar
• Esposito F., Malerba D., Semeraro G. A further study of pruning methods in decision tree induction. Preliminary Papers of the 5th International Workshop on Artificial Intelligence and Statistics (1995) (Society for AI and Statistics, Ft. Lauderdale, FL) 211–218Google Scholar
• Freund Y., Schapire R. E. A decision-theoretic generalization of online learning and an application to boosting. J. Computer and System Sciences (1997) 55:119–139Crossref, Google Scholar
• Friedman J. H. A recursive partitioning decision rule for non-parametric classification. IEEE Transactions on Computers (1977) April):404–408Crossref, Google Scholar
• Gillo M. W. MAID: A Honeywell 600 program for an automatised survey analysis. Behavioral Science (1972) 17:251–252Google Scholar
• Harrison D., Rubinfeld D. L. Hedonic prices and the demand for clean air. J. Environmental Economics and Management (1978) 5:81–102Crossref, Google Scholar
• Heath D., Kasif S., Salzberg S., Bajcsy R. Learning oblique decision trees. Proceedings of the 13th International Joint Conference on Artificial Intelligence (1993) (Morgan Kaufmann, San Mateo, CA) 1002–1007Google Scholar
• Hunt E. B., Martin J., Stone P. J.Experiments in Induction (1966) (Academic Press, New York) Google Scholar
• Kass G. V. An exploratory technique for investigating large quantities of categorical data. Applied Statistics (1980) 29:119–127Crossref, Google Scholar
• Kim H., Koehler G. J. An investigation on the conditions of pruning an induced decision tree. European J. Operational Research (1994) 77:82–95Crossref, Google Scholar
• Kumar A., Olmeda I. A study of composite or hybrid classifiers for knowledge discovery. INFORMS J. Computing (1999) 11:267–277Link, Google Scholar
• Li X.-B.Multivariate Decision Trees for Data Mining (1999) (Dept. of Management Science, University of South Carolina, Columbia, SC) . Ph.D. DissertationGoogle Scholar
• Loh W.-Y., Vanichsetakul N. Tree-structured classification via generalized discriminant analysis. J. American Statistical Association (1988) 83:715–728Crossref, Google Scholar
• Mangasarian O. L. Mathematical programming in data mining. Data Mining and Knowledge Discovery (1997) 1:183–201Crossref, Google Scholar
• Mangasarian O. L., Setiono R., Wolberg W., Coleman T. F., Li Y. Y. Pattern recognition via linear programming: theory and application to medical diagnosis. Large-Scale Numerical Optimization (1990) (SIAM Publications, Philadelphia, PA) 22–30Google Scholar
• Michie D., Spiegelhalter D. J., Taylor eds C. C.Machine Learning, Neural and Statistical Classification (1994) (Ellis Horwood, Chichester, UK) Google Scholar
• Mingers J. Expert systems—rule induction with statistical data. J. Operational Research Society (1987) 38:39–47Google Scholar
• Mingers J. An empirical comparison of pruning methods for decision tree induction. Machine Learning (1989) 4:227–243Crossref, Google Scholar
• Morgan J. N., Messenger R. C.THAID—A Sequential Analysis Program for the Analysis of Nominal Scale Dependent Variables (1973) (Institute for Social Research, University of Michigan, Ann Arbor, MI) Google Scholar
• Morgan J. N., Sonquist J. N. Problems in the analysis of survey data: and a proposal. J. American Statistical Association (1963) 58:415–434Crossref, Google Scholar
• Murphy P. M., Aha D. W.UCI Repository of Machine Learning Databases (1994) (Dept. of Information and Computer Science, University of California, Irvine, CA) . http://www.ics.uci.edu/~mlearn/MLRepository.htmlGoogle Scholar
• Murthy S. K., Kasif S., Salzberg S. A system for induction of oblique decision trees. J. Artificial Intelligence Research (1994) 2:1–32Crossref, Google Scholar
• Niblett T., Bratko I., Lavarc N. Constructing decision trees in noisy domains. Progress in Machine Learning (1986) (Sigma Press, England) Google Scholar
• Piramuthu S. Feature selection for financial credit-risk evaluation decisions. INFORMS J. Computing (1999) 11:258–266Link, Google Scholar
• Quinlan J. R. Introduction of decision trees. Machine Learning (1986) 1:81–106Crossref, Google Scholar
• Quinlan J. R. Simplifying decision trees. International J. Man-Machine Studies (1987) 27:221–234Crossref, Google Scholar
• Quinlan J. R. Unknown attribute values in induction. Proceedings of the 6th International Workshop on Machine Learning (1989) (Morgan Kaufmann, San Mateo, CA) 164–168Crossref, Google Scholar
• Quinlan J. R.C4.5: Programs for Machine Learning (1993) (Morgan Kaufmann, San Mateo, CA) Google Scholar
• Sethi I. K., Sarvarayudu G. P. R. Hierarchical classifier design using mutual information. IEEE Transactions on Pattern Analysis and Machine Intelligence (1982) 4:441–445Crossref, Google Scholar
• Sonquist J. N., Baker E. L., Morgan J. N.Searching for Structure (1971) (Institute for Social Research, University of Michigan, Ann Arbor, MI) Google Scholar
• White A. P., Liu W. Z. Bias in information-based measures in decision tree induction. Machine Learning (1994) 15:321–329Crossref, Google Scholar