Decision Bayes Criteria for Optimal Classifier Based on Probabilistic Measures  

作　　者：Wissal Drira Faouzi Ghorbel 

机构地区：[1]the Cristal Laboratory,National School of Computer Sciences,University of Manouba,Tunisia

出　　处：《Journal of Electronic Science and Technology》2014年第2期216-219,共4页电子科技学刊（英文版）

摘　　要：This paper addresses the high dimension sample problem in discriminate analysis under nonparametric and supervised assumptions. Since there is a kind of equivalence between the probabilistic dependence measure and the Bayes classification error probability, we propose to use an iterative algorithm to optimize the dimension reduction for classification with a probabilistic approach to achieve the Bayes classifier. The estimated probabilities of different errors encountered along the different phases of the system are realized by the Kernel estimate which is adjusted in a means of the smoothing parameter. Experiment results suggest that the proposed approach performs well.This paper addresses the high dimension sample problem in discriminate analysis under nonparametric and supervised assumptions. Since there is a kind of equivalence between the probabilistic dependence measure and the Bayes classification error probability, we propose to use an iterative algorithm to optimize the dimension reduction for classification with a probabilistic approach to achieve the Bayes classifier. The estimated probabilities of different errors encountered along the different phases of the system are realized by the Kernel estimate which is adjusted in a means of the smoothing parameter. Experiment results suggest that the proposed approach performs well.

关 键 词：Bayesian classifier dimension reduction kernel method optimization probabilistic dependence measure smoothing parameter 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]