Classifier Design Method Based on Piecewise Linearization
Wang Zeng · 2009
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.