Using cooperative clustering to solve multiclass problems

Abstract

In this paper, we present a multiclass classification algorithm to address the multiclass problems with cooperative clustering. Using cooperative clustering, the cluster centers of all classes can be computed iteratively and simultaneously. In the process of clustering, we select a pair of adjacent class, and make their cluster center drawn towards the boundary. Therefore, the data set around a class is found and the data set plus the data in this class can be trained to form a classifier. With this algorithm, training efficiency and classification efficiency are improved with a slight impact on classification accuracy. © 2011 Springer-Verlag Berlin Heidelberg.