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Comparison of algorithms that select features for pattern classifiers
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DOI：10.1016/S0031-3203(99)00041-2.png)
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摘要
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A comparative study of algorithms for large-scale feature selection (where the number of features is over 50) is carried out. In the study, the goodness of a feature subset is measured by leave-one-out correct-classification rate of a nearest-neighbor (1-NN) classifier and many practical problems are used. A unified way is given to compare algorithms having dissimilar objectives. Based on the results of many experiments, we give guidelines for the use of feature selection algorithms. Especially, it is shown that sequential floating search methods are suitable for small- and medium-scale problems and genetic algorithms are suitable for large-scale problems. (C) 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
Keyword:
feature selection
monotonicity
genetic algorithms
leave-one-out method
k-nearest-neighbor method
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7.6
论文数:
1.3W
被引数:
4.5W
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