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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8792


    Title: A new classification algorithm combining choquet integral and logistic regression
    Authors: Liu, Hsiang-Chan;Jheng, Yu-Du;Chen, Guey-Shya;Jeng, Bai-Cheng
    Contributors: Department of Bioinformatics
    Keywords: Control theory;Cybernetics;Integral equations;Learning systems;Logistics;Robot learning;Support vector machines;Choquet integral;Classification algorithms;Collinearity;Independent variables;Leave one outs;Logistic regression;Logistic regression algorithms;New algorithms;Real datums;Regression models;SVM;SVM algorithms
    Date: 2008
    Issue Date: 2010-04-08 12:06:09 (UTC+0)
    Publisher: Asia University
    Abstract: Logistic regression algorithm and SVM algorithm are two well-known classification algorithms but when the multi-collinearity between independent variables occurs in above two algorithms, their classifying performance will always be not good. Due to this reason, we firstly proposed a pared-down MLE method in this study to improve the logistic regression algorithm for no needing to group the original data. Secondly, we proposed a novel classification algorithm combining the Choquet integral with respect to the λ-measure based on y-support proposed by our previous work and the improved logistic regression algorithm to further improve the above situation. For evaluating the performances of the SVM, logistic regression and our new algorithm with y-support based on X-measure and P-support respectively, a real data experiment by using Leave-one-out Cross-Validation accuracy is conducted. Experimental result shows that the proposed classification algorithm combining Choquet integral regression model with y-support based on λ-measure has the best performance. ©2008 IEEE.
    Relation: Proceedings of the 7th International Conference on Machine Learning and Cybernetics, ICMLC 6 :3072-3077
    Appears in Collections:[生物資訊與醫學工程學系 ] 會議論文

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