Abstract

A novel feature selection approach is presented in this paper. Sammon’s Stress Function transforms the high dimension data to a lower dimension data set. A data set is divided into small partitions. The features are assigned randomly to these partitions. Using GA with Sammon Error as fitness value, a small desired number of features are selected from every partition. The combination of the reduced subsets of the features from these partitions is again divided into small partitions. After a certain number of iterating the process, a desired small number features is obtained. This final subset of features is tested on three classifiers Decision Tree, MLP and KNN. The classification accuracies obtained from these classifiers and the size of the reduced features sets due to the proposed method are compared with the results reported in literature. The optimistic results obtained from the proposed method justify its strength.

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