Generative/discriminative hybrid classifier based on attributes partitioning
一种基于属性分割的产生式/判别式混合分类器

In order to exploit the best of generative and discriminative approaches, on the basis of investigating a framework of generative/discriminative hybrid model based on attributes partitioning, this paper proposed a learning algorithm of generative/discriminative hybrid classifier based on attributes partitioning, GDGA. This algorithm divided the attributes X into two subsets, XG and XD, by applying genetic algorithms, and vertically partitioned the training set into two subsets DG and DD accordingly. Then it trained a generative classifier and a discriminative classifier on DG and DD respectively. In the final, it constructed a generative/discriminative hybrid classifier by combining the generative classifier and the discriminative classifier. Experimental results show that the generative/discriminative hybrid classifier performs better than its generative component and discriminative component on most data sets. This hybrid classifier has particular advantage in the case of unclear attribute distribution or not enough training data.