Research report

Somol P., Grim J.

Research Report

2295

ÚTIA AV ČR, v.v.i

Praha

2011

RNDr. Petr Somol, Ph.D. (ÚTIA - Oddělení rozpoznávání obrazů)

feature selection,, high dimensionality, ranking, generaliza

The paper addresses the problem of making dependency-aware feature selection feasible in pattern recognition problems of very high dimensionality. The idea of individually best ranking is generalized to evaluate the contextual quality of each feature in a series of randomly generated feature subsets. Each random subset is evaluated by a criterion function of arbitrary choice (permitting functions of high complexity). Eventually, the novel dependency-aware feature rank is computed, expressing the average benefit of including a feature into feature subsets. The method is efficient and generalizes well especially in very-high-dimensional problems, where traditional context-aware feature selection methods fail due to prohibitive computational complexity or to over-fitting. The method is shown well capable of over-performing the commonly applied individual ranking which ignores important contextual information contained in data.