A prototype selection algorithm using fuzzy k-important nearest neighbor method

Abstract

The k-Nearest Neighbor (KNN) algorithm is widely used as a simple and effective classification algorithm. While its main advantage is its simplicity, its main shortcoming is its computational complexity for large training sets. A Prototype Selection (PS) method is used to optimize the efficiency of the algorithm so that the disadvantages can be overcome. This paper presents a new PS algorithm, namely Fuzzy k-Important Nearest Neighbor (FKINN) algorithm. In this algorithm, an important nearest neighbor selection rule is introduced. When classifying a data set with the FKINN algorithm, the most repeated selection sample is defined as an important nearest neighbor. To verify the performance of the algorithm, five UCI benchmarking databases are considered. Experiments show that the algorithm effectively deletes redundant or irrelevant prototypes while maintaining the same level of classification accuracy as that of the KNN algorithm. © 2013 Springer Science+Business Media.