Abstract:

Feature selection is one of the methods for reduction of data sets, which improves efficiency and effectivity of machine learning. In terms of the distribution of objects and their classification labels, the continuous feature space was partitioned into a variety of subspaces, each one with a clear edge and unique classification label. After the projection of all the subspaces for  each feature, the quality of each feature was  estimated for a subspace opposite all  the other subspaces with different classification labels by means of statistical significance. Through construction of a matrix by all the estimate qualities of all features of  the subspaces, all  features were ranked from the highest classifying power to the lowest on the matrix for the feature space. After the information gain function was defined by the subset of features, the feature subset was optimally determined on the basis of ranked features by gradually adding features. Experiments on the data sets from UCI(University of California Irvine) repository by the feature selection obtained feature subsets,  by which the performance and classification accuracy of machine learning were improved, illustrating that the feature selection was feasible.

Key words: data reduction, feature selection, continuous attributes, decision table