Classification is one of the most important problems in data mining and machine learning. The quality and quantity of classification rules are two factors to influence the accuracy of classification. In this paper, we propose a new algorithm to enhance the final classification accuracy, called CMCM (Classification based on Multiple Classifier Models), which consists of two classification models. Model1 centers on the improvement of quality. The optimal attribute values are obtained as the first item of a classification rule from both the items and their complements. While in Model2, quantity is taken into consideration, so it constructs two candidate sets and uses the one-versus-many strategy to generate several rules at one time. The experiment results demonstrate that CMCM can achieve higher classification accuracy than the proposed classification approaches. CMCM can extract sufficient high-quality rules for imbalanced data. Meanwhile, it can also obtain sufficient latent information for classification.

中文翻译：

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一种基于多分类器模型的高效分类新算法

分类是数据挖掘和机器学习中最重要的问题之一。分类规则的质量和数量是影响分类准确性的两个因素。在本文中，我们提出了一种新的算法来提高最终分类的准确性，称为 CMCM（基于多个分类器模型的分类），它由两个分类模型组成。Model1 以提高质量为中心。最优属性值作为分类规则的第一项从项目及其补充中获得。而在Model2中，考虑了数量，因此它构造了两个候选集，并使用一对多的策略一次生成多个规则。实验结果表明，CMCM 可以实现比所提出的分类方法更高的分类精度。CMCM 可以为不平衡的数据提取足够的高质量规则。同时，它也可以获得足够的潜在信息进行分类。