✦ LIBER ✦

Comparison of algorithms that select features for pattern classifiers

✍ Scribed by Mineichi Kudo; Jack Sklansky

Publisher: Elsevier Science
Year: 2000
Tongue: English
Weight: 506 KB
Volume: 33
Category: Article
ISSN: 0031-3203
DOI: 10.1016/s0031-3203(99)00041-2

No coin nor oath required. For personal study only.

✦ Synopsis

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-classi"cation rate of a nearestneighbor (1-NN) classi"er and many practical problems are used. A uni"ed 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 #oating search methods are suitable for small-and medium-scale problems and genetic algorithms are suitable for large-scale problems.

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