Abstract
Genetic algorithms with a novel encoding scheme for feature selection are introduced. The proposed genetic algorithm is restricted to a particular predetermined feature subset size where the local optimal set of features is searched for. The encoding scheme limits the length of the individual to the specified subset size, whereby each gene has a value in the range from 1 to the total number of available features.
This article also gives a comparative study of suboptimal feature selection methods using real-world data. The validation of the optimized results shows that the true feature subset size is significantly smaller than the global optimum found by the optimization algorithms.
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References
Bäck, T.: Evolutionary Algorithms in Theory and Practice. Oxford University Press (1996)
Baker, L.E.: Reducing bias and inefficiency in the selection algorithm. Proceedings of the 2 nd International Conference on Genetic Algorithms and Their Applications. Lawrence Erlbaum Associates (1987) 14–21
Dash, M., Liu, H.: Feature Selection for Classification. Intelligent Data Analysis, 1 (1997) 131–156
Devijver, P.A., Kittler, J.: Pattern Recognition: A statistical approach. Prentice Hall International (1982)
Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley-Interscience Publications (1973)
Ferri, F.J., Pudil, P., Hatef, M., Kittler, J.: Comparative study of techniques for large-scale feature selection. In: Gelsema, E.S., Kamal, L.N. (eds.): Pattern Recognition in Practice IV. Elsevier Science B.V. (1994) 403–413
Jain, A., and Zongker, D.: Feature Selection: Evaluation, Application, and Small Sample Performance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19 (1997) 153–158
Kittler, J.: Feature set search algorithms. In: Chen, C.H. (ed.): Pattern Recognition and Signal Processing. Sijthoff and Noordhoff, Alphen aan den Rijn (1978) 41–60
Kohavi, R.: A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In: Proc. IJCAI-95 (1995) 1146–1151
Michalewicz, Z., Fogel, D.B.: How to solve it: Modern Heuristics. Springer-Verlag, Berlin Heidelberg (2000)
Pernkopf, F.: Automatic Inspection System for Detection and Classification of Flaws on turned Parts. Technical Report. University of Leoben (2000)
Pudil, P., Novovičová, J., Kittler, J.: Floating search methods in feature selection. Pattern Recognition Letters, 15 (1994) 1119–1125
Raudys, S.J., Jain, A.K.: Small sample size effects in statistical pattern recognition: Recommendations for practitioners. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13 (1991) 252–264
Siedlecki, W., and Sklansky, J.: A note on genetic algorithms for large-scale feature selection. Pattern Recognition Letters, 10 (1989) 335–347
Somol, P., Pudil, P., Novovičová, J., Paclík, P.: Adaptive floating search methods in feature selection. Pattern Recognition Letters, 20 (1999) 1157–1163
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Pernkopf, F., O’Leary, P. (2001). Feature Selection for Classification Using Genetic Algorithms with a Novel Encoding. In: Skarbek, W. (eds) Computer Analysis of Images and Patterns. CAIP 2001. Lecture Notes in Computer Science, vol 2124. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44692-3_20
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DOI: https://doi.org/10.1007/3-540-44692-3_20
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