Comparison of algorithms that select features for pattern classifiers
Pattern RecognitionPublished 1 January 2000
Mineichi Kudo, Jack Sklansky
Citations889
SJR quartileQ1
SJR score2.06
SNIP2.67
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TL;DR
It is shown that sequential floating search methods are suitable for small- and medium-scale problems and genetic algorithms are suitableFor large-scale feature selection algorithms, the goodness of a feature subset is measured by leave-one-out correct-classification rate of a nearest-neighbor (1-NN) classifier.
Abstract
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.
Keywords
Computer ScienceEngineeringEnvironmental Science
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