Abstract
Quality and quantity are�the�two�key�factors�to�influence the accuracy of classification. In order to improve the classification accuracy, in this paper, we propose a new algorithm, called CMCM (Classification based on Multiple Classifier Models), which consists of two classification models. In Model1, we mainly focus on the improvement of quality, thus the best attribute value from both the items and their complements in the training set is selected as the first item of a classification rule. 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 show that: (1) Model1 can extract sufficient high quality rules and achieve high classification accuracy. (2) Model2 can extract sufficient information and achieve high classification accuracy. (3) CMCM can achieve higher classification accuracy compare with traditional classification.
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Acknowledgments
This work is supported by Natural Science Funds of China (Nos. 61701213), Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, the Program for Excellent Talents of Fujian Province, the Special Research Fund for Higher Education of Fujian (No. JK2015027), and the Research Fund for Educational Department of Fujian Province (No. JA15300).
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Zheng, Y., Li, G., Zhang, W. (2018). A New Algorithm for Classification Based on Multi-classifiers Learning. In: Yuan, H., Geng, J., Liu, C., Bian, F., Surapunt, T. (eds) Geo-Spatial Knowledge and Intelligence. GSKI 2017. Communications in Computer and Information Science, vol 849. Springer, Singapore. https://doi.org/10.1007/978-981-13-0896-3_25
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DOI: https://doi.org/10.1007/978-981-13-0896-3_25
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