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Feature Subset Selection Algorithm based on Entropy  

홍석미 (경희대학교 컴퓨터공학과)
안종일 (용인송담대학교 컴퓨터소프트웨어)
정태충 (경희대학교 컴퓨터공학과)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.41, no.2, 2004 , pp. 87-94 More about this Journal
Abstract
The feature subset selection is used as a preprocessing step of a teaming algorithm. If collected data are irrelevant or redundant information, we can improve the performance of learning by removing these data before creating of the learning model. The feature subset selection can also reduce the search space and the storage requirement. This paper proposed a new feature subset selection algorithm that is using the heuristic function based on entropy to evaluate the performance of the abstracted feature subset and feature selection. The ACS algorithm was used as a search method. We could decrease a size of learning model and unnecessary calculating time by reducing the dimension of the feature that was used for learning.
Keywords
ACS; machine learning; feature subset selection; classification; Entropy; Ant Colony System;
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  • Reference
1 G. H. John, R. Kohavi, and P. Pfleger. Irrelevant features and the subset selection problem, In Proc. of 11th Int'l Conf. On Machine Learning, p 121-129, San Mateo, CA, 1994, Morgan Kaufmann
2 K. kira and L. A. Rendell. The feature selection problem: Traditional methods and a new algorithm. In 10th National Conference on Artificial Intelligence, p 129-134. MIT Press, 1992
3 Y -S Kim, W. N. Street and F. Menczer. Meta-Evolutionary Ensembles. In Proc. 2002 Int'l Joint Conf. on Neural Networks(IJCNN -02), pp 2791-2796, 2002   DOI
4 M. A. Hall. Correlation-based Feature Selection for Machine Learning, Ph. D diss. Hamilton, NZ: Waikato University, Department of Computer Science
5 A. Colomi, M. Dorgio and V. Maniezzo, Distributed optimization by ant colonies, Proceedings of ECAL91-Euripean Conference on Artificial Life, Paris, France, F. Vardla and P. Bourgine(Eds.), Elsevier Publishing, p 134-142, 1991
6 J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81-100, 1986
7 D. Opitz. Feature selection for ensemble. In 16th National Conf. on Artificial Intellignecr (AAAI), pp 379-384, Orlando, FL, 1999
8 원동호 역, 정보와 부호이론, 도서출판 ohm, 1997
9 UCI Repository of Machine Learning Data-bases.httpi//www.ics.uci.edu/~mleam/MLfiepository.html]
10 R. Beckers, J. L. Deneubourg and S. Goss, Trails and U-turns in the selection of the shortest path bye the ant Lasius Niger, Journal of Theoretical Biology, vol. 159, p 397-415, 1992   DOI
11 Utgoff P. An improved algorithm for incremental induction of decision trees. In Proceedings of the Eleventh International Conference on Machine Learning, p 318-325, 1994