Increasing classification efficiency with multiple mirror classifiers

Reducing the computational load for training and classification procedures is a major problem in many pattern recognition approaches, such as artificial neural networks and support vector machines. Combining the multiple mirror classifiers is proven to be an efficient way to reduce the classification time. In this paper, we propose an approach that uses cooperative clustering method to construct mirror classifiers. With this procedure, the set of mirror point pairs with pre-determined size near the boundary of two classes is determined. Each mirror point pair constructs a small classifier. The minimum squared error based method and support vector machine based method are proposed to determine the weights for combining the multiple mirror classifiers. Experiments show that the training efficiency and classification efficiency are improved with a slight impact on generalization performance.