An efficient reduction algorithm based on natural neighbor and nearest enemy

Abstract

Prototype reduction is aimed at reducing prohibitive computational costs and the storage space for pattern recognition. The most frequently used methods include the condensating and editing approaches. Condensating method removes the patterns far from the decision boundary and do not contribute to better classification accuracy, while editing method removes noise patterns to improve the classification accuracy. In this paper, a new hybrid algorithm called prototype reduction algorithm based on natural neighbor and nearest enemy is presented. At first, an editing algorithm is proposed to filter noisy patterns and smooth the class boundaries by using the concept of natural neighbor. The main advantage of the editing algorithm is that it does not require any user-defined parameters. Then, using a new condensing method based on nearest enemy to reduce prototypes far from decision line. Through this algorithm, interior prototypes are discarded. Experiments show that the hybrid approach effectively reduces the number of prototypes while achieves higher classification performance along with competitive prototype algorithms.