基于Fisher判别分析的贝叶斯分类器

摘要

Classical Bayesian classifier which satisfies the assumption of condition attributes independent of each other can not use between-class information effectively. In order to solve this problem, an improved algorithm of Bayesian classifier combined with Fisher Linear Discriminant Analysis(FLDA) is proposed. This algorithm is the key to search the projection space of maximum separation. The original samples are projected to maximum separation space and new samples are obtained. These new samples are classifed by Bayesian classifier. Experimental results show that improved Bayesian classifier has higher accuracy of classification and better performance of classification in the given data collection.%针对满足"类条件属性相互独立"假定的经典贝叶斯分类器无法有效利用类间信息的缺陷,结合Fisher线性判别分析,给出一种基于Fisher线性判别分析的贝叶斯分类器的改进算法.该算法通过寻找类与类最大分离的投影空间,将原样本向最大分离空间投影,以获得新样本,并采用贝叶斯分类器对新样本进行分类.实验结果表明,在给定的数据集上,该贝叶斯分类器的分类正确率较高,分类性能较好.