Using Rough Sets and Maximum Similarity Graphs for Nearest Prototype Classification

The nearest neighbor rule (NN) is one of the most powerful yet simple non parametric classification techniques. However, it is time consuming and it is very sensitive to noisy as well as outlier objects. To solve these deficiencies several prototype selection methods have been proposed by the scientific community. In this paper, we propose a new editing and condensing method. Our method combines the Rough Set theory and the Compact Sets structuralizations to obtain a reduced prototype set. Numerical experiments over repository databases show the high quality performance of our method according to classifier accuracy.