Abstract
In order to make a search for good variable subsets, one has to know which subsets are good and which are not. In other words, an evaluation mechanism for an individual variable subset needs to be defined first.
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References
D. W. Aha and R. L. Bankert. A comparative evaluation of sequential feature selection algorithms. In D. Fisher and J.-H. Lenz, editors, Artificial Intelligence and Statistics V, pages 199–206. Springer-Verlag, 1996.
T. M. Cover and J. M. van Campenhout. On the possible orderings in the measurement selection problem. IEEE Transactions on Systems, Man and Cybernetics, 7(9):657–661, 1977.
I. Guyon and A. Elisseeff. An introduction to variable and feature selection. Journal of Machine Learning Research, 3:1157–1182, 2003.
A. K. Jain and D. Zongker. Feature selection: Evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 19(2):153–158, 1997.
D. D. Jensen and P. R. Cohen. Multiple comparisons in induction algorithms. Machine Learning, 38(3):309–338, 2000.
S. Kirkpatrick, C. D. Gelatt, and M. P. Vecchi. Optimization by simulated annealing. Science, 220(4598):671–680, 1983.
J. Kittler. Feature set search algorithms. In C. H. Chen, editor, Pattern Recognition and Signal Processing, pages 41–60. Sijthoff & Noordhoff, 1978.
R. Kohavi and G. John. Wrappers for feature subset selection. Artificial Intelligence, 97(1–2):273–324, 1997.
R. Kohavi and D. Sommerfield. Feature subset selection using the wrapper model: Overfitting and dynamic search space topology. In Proc. of the 1st Int. Conf. on Knowledge Discovery and Data Mining (KDD-95), pages 192–197, Montreal, Canada, 1995.
M. Kudo and J. Sklansky. Comparison of algorithms that select features for pattern classifiers. Pattern Recognition, 33(1):25–41, 2000.
T. Marill and D. M. Green. On the effectiveness of receptors in recognition systems. IEEE Transactions on Information Theory, 9(1):11–17, 1963.
Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer-Verlag, 1992.
P. M. Narendra and K. Fukunaga. A branch and bound algorithm for feature subset selection. IEEE Transactions on Computers, 26(9):917–922, 1977.
A. Y. Ng. On feature selection: Learning with exponentially many irrelevant features as training examples. In Proc. of the 15th Int. Conf. on Machine Learning (ICML-98), pages 404–412, Madison, WI, USA, 1998.
P. Pudil, J. Novovičová, and J. Kittler. Floating search methods in feature selection. Pattern Recognition Letters, 15(11):1119–1125, 1994.
J. Reunanen. Overfitting in making comparisons between variable selection methods. Journal of Machine Learning Research, 3:1371–1382, 2003.
J. Reunanen. A pitfall in determining the optimal feature subset size. In Proc. of the 4th Int. Workshop on Pattern Recognition in Information Systems (PRIS 2004), pages 176–185, Porto, Portugal, 2004.
W. Siedlecki and J. Sklansky. On automatic feature selection. International Journal of Pattern Recognition and Artificial Intelligence, 2(2):197–220, 1988.
W. Siedlecki and J. Sklansky. A note on genetic algorithms for large-scale feature selection. Pattern Recognition Letters, 10(5):335–347, 1989.
P. Somol and P. Pudil. Oscillating search algorithms for feature selection. In Proc. of the 15th Int. Conf. on Pattern Recognition (ICPR’2000), pages 406–409, Barcelona, Spain, 2000.
P. Somol, P. Pudil, and J. Kittler. Fast branch & bound algorithms for optimal feature selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(7):900–921, 2004.
P. Somol, P. Pudil, J. Novovičová, and P. Paclík. Adaptive floating search methods in feature selection. Pattern Recognition Letters, 20(11–13):1157–1163, 1999.
S. D. Stearns. On selecting features for pattern classifiers. In Proc. of the 3rd Int. Joint Conf. on Pattern Recognition, pages 71–75, Coronado, CA, USA, 1976.
D. J. Stracuzzi and P. E. Utgoff. Randomized variable elimination. Journal of Machine Learning Research, 5:1331–1362, 2004.
A. W. Whitney. A direct method of nonparametric measurement selection. IEEE Transactions on Computers, 20(9):1100–1103, 1971.
B. Yu and B. Yuan. A more efficient branch and bound algorithm for feature selection. Pattern Recognition, 6(26):883–889, 1993.
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Reunanen, J. (2006). Search Strategies. In: Guyon, I., Nikravesh, M., Gunn, S., Zadeh, L.A. (eds) Feature Extraction. Studies in Fuzziness and Soft Computing, vol 207. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-35488-8_5
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DOI: https://doi.org/10.1007/978-3-540-35488-8_5
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