language: en

A Monotonic Measure for Optimal Feature Selection

Huan Liu 1 and Hiroshi Motoda 2 and Manoranjan Dash 3
1 Dept of Info Sys & Comp Sci, National University of Singapore,
Singapore 119260.
2 Division of Intelligent Sys Sci, Osaka University,
Ibaraki, Osaka 567, Japan.
3 BioInformatics Centre, National University of Singapore,
Singapore 119074.
fliuh, manoranjg@iscs.nus.edu.sg motoda@sanken.osaka-u.ac.jp

Abstract. Feature selection is a problem of choosing a subset of relevant features. In general, only exhaustive search can bring about the optimal subset. With a monotonic measure, exhaustive search can be avoided without sacrificing optimality. Unfortunately, most error- or distance-based measures are not monotonic. A new measure is employed in this work that is monotonic and fast to compute. The search for relevant features according to this measure is guaranteed to be complete but not exhaustive. Experiments are conducted for verification.

1 Introduction