Selection of continuous features based on distribution of objects

A novel feature selection approach is proposed for data space defined over continuous features, which obtains a subset of features,such that it can discriminate class labels of objects and its discriminant ability is not inferior to that of the original features,so to effectively improve the learning performance and intelligibility of the classification model.According to the spatial distribution of objects and their classification labels,a data space with continuous features is partitioned into subspaces,each with a clear edge and a single classification label.Then these labelled subspaces are projected to each continuous feature.The measurement of each feature is estimated for a subspace against all other subspace-projected features by means of statistical significance.Through the construction of a matrix of the measurements of the subspaces by all features,the subspace-projected features are ranked in a descending order based on the discriminant ability of each feature in the matrix.After evaluating a gain function of the discriminant ability defined by the best-so-far feature subset,the resulting feature subset can be incrementally determined. Our comprehensive experiments on the UCI Repository data sets have demonstrated the effectiveness and efficiency of the proposed approach of feature selection