Publication details

Fast Dependency-Aware Feature Selection in Very-High-Dimensional Pattern Recognition Problems

Research Report

Somol Petr, Grim Jiří


publisher: ÚTIA AV ČR, v.v.i, (Praha 2011)

edition: Research Report 2295

research: CEZ:AV0Z10750506

project(s): 1M0572, GA MŠk, 2C06019, GA MŠk

keywords: feature selection,, high dimensionality, ranking, generalization, over-fitting, stability, classification, pattern recognition, machine learning

preview: Download

abstract (eng):

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.

RIV: BD