北京科技大学学报2012，Vol.34Issue(1)：26-30,5.
一种改进的最大相关最小冗余选择性贝叶斯分类器
An improved maximum relevance and minimum redundancy selective Bayesian classifier
摘要
Abstract
A kind of improved mRMR SBC was proposed by using K-means clustering and incremental learning algorithms to enlarge the scale of training samples. On one hand, the testing samples are labeled using the K-means clustering algorithm and are added to the training set. A regulatory factor is introduced into the process of attribute selection to reduce the risk of mislabel resulting from K- means clustering. On the other hand, some samples that are most helpful for improving the current classification accuracy are selected from the testing set and are added to the training set. Based on the enlarged training set, parameters in the Bayesian classifier are adjusted incrementally. Experimental results show that compared with mRMR SBC, the proposed Bayesian classifier has better classification results and is applicable for solving the classification problem for the high-dimensional dataset with little labels.关键词
分类器/属性选择/冗余/K均值聚类/增量学习Key words
classifiers/attribute selection/redundancy/K-means clustering/incremental learning分类
计算机与自动化引用本文复制引用
马勇,仝瑶瑶,程玉虎..一种改进的最大相关最小冗余选择性贝叶斯分类器[J].北京科技大学学报,2012,34(1):26-30,5.基金项目
国家自然科学基金资助项目（60804022 （）
60974050 （）
61072094） （）
教育部新世纪优秀人才支持计划资助项目 （）
