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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.