Abstract
The ranking of features according to a feature selection criteria is examined. The concept of ϵ-equivalence is introduced to measure the extent to which a ranking deviates from the ranking induced by the probability of error rule. The relationship between the ϵ-equivalence of a given rule and the bounds on the probability of error derived from this rule is demonstrated. Illustrations of the ϵ-equivalence concept are presented for Shannon's equivocation rule, the quadratic equivocation rule, and the Bhattacharyya rule. A numerical example concludes the presentation.
Original language | English |
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Pages (from-to) | 769-772 |
Number of pages | 4 |
Journal | IEEE Transactions on Information Theory |
Volume | 24 |
Issue number | 6 |
DOIs | |
State | Published - Nov 1978 |