Abstract
Feature analysis and feature selection are fundamental pursuits in pattern recognition. We revisit and generalize an issue of feature selection by introducing a mechanism of soft (fuzzy) feature selection. The underlying idea is to consider features to be granular rather than numeric. By varying the level of granularity, we modify the level of contribution of the specific feature to the overall feature space. We admit an interval model of the features meaning that their values assume a form of numeric intervals. The intervalization of the features exhibits a clear-cut interpretation. Moreover a contribution of the features to the formation of the feature space can be easily controlled: the broader the interval, the less essential contribution of the feature to the entire feature space. In limit, when the intervals get broad enough, one may view the feature to be completely eliminated (dropped) from the feature space. The quantification of the features in terms of their importance is realized in the setting of the clustering FCM model (namely, a process of the binary or fuzzy feature selection is carried out and numerically quantified in the space of membership values generated by fuzzy clusters). As the focal point of this study concerns an interval-like form of information granules, we reveal how such feature intervalization helps approximate fuzzy sets described by any type of membership function. Detailed computations give rise to a detailed quantification of such granular features. Numerical experiments provide a comprehensive numerical illustration of the problem.