Feature selection for multiple binary classification problems

Abstract The present study proposes an unsupervised method for selection of feature subsets, which retain sufficient information for classification purposes. Multiple alternative physically feasible partitions can be dealt with, using the present method. The method is based on an alternative approach to data representation, in which the axes are the data points instead of the features (a transpose projection). Under this representation, coherent features are located in the vicinity of each other, and hence can be clustered, while noisy features are pointed out and eliminated. The method bypasses the “curse of dimensionality” and demonstrates good results in particular in small data sets.

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