On Comparing Feature Reduction Techniques for Accuracy Improvement of the k-NN Classification

The aim of this paper is to perform a comparative study of feature reduction techniques that are most appropriate for the classification with k-nearest neighbor and tested with medical data. Medical data are normally high-dimensional in their nature. Their high dimensionality property can affect performance of the classification process. In this work, we perform various feature reduction techniques implemented with Matlab to decrease dimensions of data before the k-nearest neighbor classification step. From the experimented results, we found that best performance is obtained from using the PCA algorithm to reduce features of data. The comparison in terms of accuracy turns out that PCA and ROC feature reduction techniques can improve the classification prediction, where the t-test feature reduction has very limited effect over the classification accuracy.