Publication details

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

Conference Paper (international conference)

Somol Petr, Grim Jiří, Pudil P.

serial: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2011), p. 502-509

action: The 2011 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2011), (Anchorage, Alaska, US, 09.10.2011-12.10.2011)

research: CEZ:AV0Z10750506

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

keywords: feature selection, high dimensionality, ranking, classification, machine learning

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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.