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

Typ:
Research report
Name of edition:
Research Report
Article number:
2295
Publisher:
ÚTIA AV ČR, v.v.i
Serie:
Praha
Year:
2011
Keywords:
feature selection,, high dimensionality, ranking, generaliza
Anotation:
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.
 
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