A New Efficient Algorithm Based on Multi-Classifiers Model for Classification
Yifeng Zheng, Guohe Li, Wenjie Zhang · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · 2020
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.