A Fast Multiclass Classification Algorithm Based on Cooperative Clustering

We present a fast multiclass classification algorithm to address the multiclass problems with a new clustering method, namely cooperative clustering. In the method of cooperative clustering, we iteratively compute the cluster centers of all classes simultaneously. For every cluster center in a class, a cluster center in an adjacent class is selected and the pair of cluster centers is drawn towards the boundary. In this way, 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 cooperative clustering, one binary classifier in the one-vs-all approach can be trained with far less samples. Furthermore, a kNN method is proposed to accelerate the classifying procedure. With this algorithm, both training and classification efficiency are improved with a slight impact on classification accuracy.