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Feature selection algorithms in classification problems : an experimental evaluation

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Feature selection (FS) is a significant topic for the development of efficient pattern recognition systems. FS refers to the selection of the most appropriate subset of features that describes (adequately) a given classification task. The objective of this paper is to perform a thorough analysis of the performance and efficiency of feature selection algorithms (FSAs). The analysis covers a variety of important issues with respect to the functionality of FSAs, such as: (a) their ability to identify relevant features, (b) the performance of the classification models developed on a reduced set of features, (c) the reduction in the number of features, and (d) the interactions between different FSAs with the techniques used to develop a classification model. The analysis considers a variety of FSAs and classification methods.
Rocznik
Strony
331--349
Opis fizyczny
Bibliogr. 31 poz.
Twórcy
autor
  • Technical University of Crete, Dept. of Production Engineering and Management, Financial Engineering Laboratory, University Campus, 73100 Chania, Greece
autor
  • Technical University of Crete, Dept. of Production Engineering and Management, Financial Engineering Laboratory, University Campus, 73100 Chania, Greece
  • Technical University of Crete, Dept. of Production Engineering and Management, Financial Engineering Laboratory, University Campus, 73100 Chania, Greece
Bibliografia
  • [1] Almuallim, Н., Dietterich, T.G., Learning with many irrelevant features, in: Proceedings of the 9th National Conference on Artificial Intelligence (vol 2), Anaheim, CA, AAAI Press, 1991, 547-552.
  • [2] Almuallim, H., Dietterich, T.G., Learning boolean concepts in the presence of many irrelevant features, Artificial Intelligence, 69, 1-2, 1994, 279-305.
  • [3] Ben-Bassat, M., Use of distance measures, information measures and error bounds in feature evaluation, in: Krishnaiah, P.R., Kanal, L.N. (eds.), Handbook of Statistics, North Holland, 1982, 773-791.
  • [4] Blum, A.L., Langley, P., Selection of relevant features and examples in machine learning, Artificial Intelligence, 97, 1-2, 1997, 245-271.
  • [5] Breiman, L., Friedman, J.H., Olsen, R.A., Stone, C.J., Classification and Regression Trees, Pacific Grove, California, 1984.
  • [6] Choubey, S. K., Deogun, J. S., Raghavan, V. V., Sever, H., A comparison of feature selection algorithms in the context of rough classifiers, in: Proceedings of the 5th IEEE International Conference on Fuzzy Systems (vol. 2), New Orleans, LA, 1996, 1122-1128.
  • [7] Dash, M., Liu, H., Hybrid search of feature subsets, in: H.Y. Lee, H. Motoda (eds.), Proceedings of the 15th Pacific Rim International Conference on Artificial Intelligence, Springer Verlag, Singapore, 1998, 22-27.
  • [8] Dietterich, T.G., Bakiri G., Solving multiclass learning problems via error-correcting output codes, Journal of Artificial Intelligence Research, 2, 1995, 263-286.
  • [9] Doak, J., An evaluation of feature selection methods and their application to computer security, Technical report CSE-92-18, University of California, Department of Computer Science, Davis, CA, 1992.
  • [10] Duda, R.O., Hart, P.E., Stork, D.G., Pattern Classification (2nd Edition), John Wiley, New York, 2001.
  • [11] Efron, В., Estimating the error rate of a prediction rule: Improvement of cross-validation, Journal of the American Statistical Association, 78, 1983, 316-330.
  • [12] Fukunaga, K., Introduction to Statistical Pattern Recognition (2nd edition), Academic Press, San Diego, 1990.
  • [13] Hosmer, D.W., Lemeshow, S., Applied Logistic Regression (2nd Edition), John Wiley, New York, 2000.
  • [14] Kira, K., Rendell, L., The feature selection problem: Traditional methods and a new algorithm, in: Proceedings of AAAI-92, AAAI Press, 1992, 129-134.
  • [15] Klecka, W.R., Discriminant Analysis, Sage Publications, London, 1980.
  • [16] Kohavi, R., John, G.H., Wrappers for feature subset selection, Artificial Intelligence, 97, 1-2, 1997, 273-324.
  • [17] Koller, D., Sahami, M., Toward optimal feature selection, in: L. Saitta (ed.), Proceedings of the 13th International Conference on Machine Learning, Morgan Kaufmann, San Francisco, 1996, 284-292.
  • [18] Liu, H., Motoda, H., Feature Selection for Knowledge Discovery and Data Mining, Kluwer Academic Publishers, London, 1998.
  • [19] Liu, H., Setiono, R., A probabilistic approach to feature selection: A filter solution, in: L. Saitta (ed.). Proceedings of the 13th International Conference on Machine Learning, Morgan Kaufmann, San Francisco, 1996a, 319-327.
  • [20] Liu, H., Setiono, R., Scalable feature selection for large sized databases, in: Proceedings of the 4th World Congress on Expert Systems, Morgan Kaufmann, 1998b, 68-75.
  • [21] Molina, L.C., Belanche, L., Nebot, A., Feature selection algorithms: A survey and experimental evaluation, in: Proceedings of the 2002 IEEE International Conference on Data Proceedings of the 2002 IEEE International Conference on Data Mining, IEEE Computer Society, 2002, 306-313.
  • [22] Moody, J., The effective number of parameters: An analysis of generalization and regularization in nonlinear learning systems, in: J. Moody, S. Hanson, R. Lippmann (eds). Advances in Neural Information Processing Systems, Morgan Kaufmann, 1992, 847-854.
  • [23] Narendra, P., Fukunaga, K., A branch and bound algorithm for feature subset selection, IEEE Transactions on Computers, 26, 9, 1977, 917-922.
  • [24] Pawlak, Z., Rough sets. International Journal of Information and Computer Sciences, 11, 1982, 341-356.
  • [25] Pudil, P., Novovicova, J., Kittler, J., Floating search methods in feature selection, Pattern Recognition Letters, 15, 11, 1994, 1119-1125.
  • [26] Quinlan, J.R., Induction of decision trees. Machine Learning, 1, 1, 1986, 81-106.
  • [27] Quinlan, J.R., C4.5: Programs for Machine Learning, Morgan Kaufmann, 1993.
  • [28] Specht, D.F., Probabilistic neural networks, Neural Networks 3, 1990, 109-118.
  • [29] Stone, M., Cross-validation choice and assessment of statistical predictions, Journal of the Royal Statistical Society B, 36, 1974, 111-147.
  • [30] Webb, A., Statistical Pattern Recognition (2nd Edition), John Wiley, New York, 2002.
  • [31] Vapnik, V.N., Statistical Learning Theory, John Wiley, New York, 1998.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPP1-0053-0099
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