Classifier design method based on piecewise linearization

The minimax risk criterion based decision is an important method for making decisions when priori probabilities are unknown. However, the performance of a minimax risk criterion based classifier is poor in most cases. To improve the performance of the designed classifier, a piecewise linearization based design method is presented. Firstly, the proposed method makes a rough estimation of the prior probability. Then, it decides the right interval where the estimated prior lies. Finally, the corresponding classifier is employed to make a decision. The theoretical deduction and experimental results show that the presented method is efficient and the performance of the corresponding classifier designed by the method approaches to Bayesian classifier.