Abstract
Several recent machine learning publications demonstrate the utility of using feature selection algorithms in supervised learning tasks. Among these, sequential feature selection algorithms are receiving attention. The most frequently studied variants of these algorithms are forward and backward sequential selection. Many studies on supervised learning with sequential feature selection report applications of these algorithms, but do not consider variants of them that might be more appropriate for some performance tasks. This paper reports positive empirical results on such variants, and argues for their serious consideration in similar learning tasks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Aha, D. W. (1992). Generalizing from case studies: A case study. In Proceedings of the Ninth International Conference on Machine Learning (pp. 1–10). Aberdeen, Scotland: Morgan Kaufmann.
Aha, D. W., & Bankert, R. L. (1994). Feature selection for case-based classification of cloud types: An empirical comparison. In D. W. Aha (Ed.) Case-Based Reasoning: Papers from the 1994 Workshop (Technical Report WS-94–01). Menlo Park, CA: AAAI Press.
Almuallim, H., & Dietterich, T. G. (1991). Learning with many irrelevant features. In Proceedings of the Ninth National Conference on Artificial Intelligence (pp. 547–552). Menlo Park, CA: AAAI Press.
Bankert, R. L. (1994a). Cloud classification of AVHRR imagery in maritime regions using a probabilistic neural network. Journal of Applied Meteorology, 33,909–918.
Bankert, R., L. (1994b). Cloud pattern identification as part of an automated image analysis. Proceedings of the Seventh Conference on Satellite Meteorology and Oceanography (pp. 441–443). Boston, MA: American Meteorological Society.
Caruana, R & Freitag, D. (1994). Greedy attribute selection. In Proceedings of the Eleventh International Machine Learning Conference (pp. 28–36). New Brunswick, NJ: Morgan Kaufmann.
Cover, T. M., & van Campenhout, J. M. (1977). On the possible orderings in the measurement selection problem. IEEE Transactions on Systems Man and Cybernetics, 7, 657–661.
Doak, J. (1992). An evaluation of feature selection methods and their application to computer security (Technical Report CSE-92–18). Davis, CA: University of California, Department of Computer Science.
Fu, K. S. (1968). Sequential methods in pattern recognition and machine learning. New York: Academic Press.
John, G., Kohavi, R., & Pfleger, K. (1994). Irrelevant features and the subset selection problem. In Proceedings of the Eleventh International Machine Learning Conference (pp. 121–129). New Brunswick, NJ: Morgan Kaufmann.
Kononenko, I. (1994). Estimating attributes: Analysis and extensions of RELIEF. In Proceedings of the 1994 European Conference on Machine Learning (pp. 171–182). Catania, Italy: Springer Verlag.
Langley, P., & Sage, S. (1994). Oblivious decision trees and abstract cases. In D. W. Aha (Ed.), Case-Based Reasoning: Papers from the 1994 Workshop (Technical Report WS-94–01). Menlo Park, CA: AAAI Press.
Milligan, G. W., & Cooper, M. C. (1985). An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50, 159–179.
Moore, A. W., & Lee, M. S. (1994). Efficient algorithms for minimizing cross validation error. In Proceedings of the Eleventh International Conference on Machine Learning (pp. 190–198). New Brunswick, NJ: Morgan Kaufmann.
Mucciardi, A. N., & Gose, E. E. (1971). A comparison of seven techniques for choosing subsets of pattern recognition properties. IEEE Transaction on Computers, 20, 1023–1031.
Skalak, D. (1994). Prototype and feature selection by sampling and random mutation hill climbing algorithms. In Proceedings of the Eleventh International Machine Learning Conference (pp. 293–301). New Brunswick, NJ: Morgan Kaufmann.
Townsend-Weber, T., & Kibler, D. (1994). Instance-based prediction of continuous values. In D. W. Aha (Ed.), Case-Based Reasoning: Papers from the 1994 Workshop (Technical Report WS-94–01). Menlo Park, CA: AAAI Press.
Vafaie, H., & De Jong, K. (1993). Robust feature selection algorithms. In Proceedings of the Fifth Conference on Tools for Artificial Intelligence (pp. 356–363). Boston, MA: IEEE Computer Society Press.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1996 Springer-Verlag New York, Inc.
About this chapter
Cite this chapter
Aha, D.W., Bankert, R.L. (1996). A Comparative Evaluation of Sequential Feature Selection Algorithms. In: Fisher, D., Lenz, HJ. (eds) Learning from Data. Lecture Notes in Statistics, vol 112. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2404-4_19
Download citation
DOI: https://doi.org/10.1007/978-1-4612-2404-4_19
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-94736-5
Online ISBN: 978-1-4612-2404-4
eBook Packages: Springer Book Archive