Feature selection as an area of interest within pattern
recognition, deals with selection of a subset of attributes used in
construction of a model describing observations. The purpose of
this stage includes reducing data dimensionality by removing
irrelevant and redundant features, reducing the amount of learning
data, improving predictive accuracy and comprehensibility of a
classification hypothesis. This book introduces Feature Usability
Index (FUI) as a measure for evaluating classification efficacy of
features and its application in feature selection. Experimental
applications presents optimal feature subset selection through
ordering based on FUI, use of FUI for ranking and selection of
feature extraction techniques for a specific linguistic feature,
and Color Usability Index as a measure for predicting performance
of image segmentation algorithms.
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