Research on construction of base classifiers based on discretization method for ensemble learning
集成学习中基于离散化方法的基分类器构造研究

Construction method of base classifiers based on data discretizaion was proposed to produce individual classifiers with good diversity in ensemble learning. And then it was used in support vector machines ensemble. Using the rough sets and Boolean reasoning algorithm to process the training samples, this method can eliminate the irrelative and redundant attributes to improve the accuracy and diversity of base classifiers. Experimental results show that the presented method can achieve better performance than the traditional ensemble learning methods such as Bagging and Adaboost.