An evolutionary attribute clustering and selection method based on feature similarity

In the past, we proposed a GA-based clustering method for attribute clustering and feature selection. The fitness of each individual was evaluated using both the average accuracy of attribute substitutions in clusters and the cluster balance. The evaluation was, however, quite time-consuming. In this paper, we modify the previous method for a better execution performance based on feature similarity and feature dependence. The fitness of a chromosome combines both the total degrees of similarity between pairs of features and the accuracy of centers rather than the average accuracy by all the combinations. Experimental results also show the performance of the proposed approach.