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http://asiair.asia.edu.tw/ir/handle/310904400/8780
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| Title: | Choquet integral regression model based on high-order l-measure |
| Authors: | Liu, Hsiang-Chuan;Chen, Wei-Sung;Tu, Yu-Chieh;Yu, Yen-Kuei |
| Contributors: | Department of Bioinformatics |
| Keywords: | Control theory;Cybernetics;Linear regression;Mean square error;Robot learning;Choquet integral;Choquet integral regression model;Closed form;Cross validation;Forecasting models;Fuzzy measures;High-order;L-measure;L-measure Lh-measure;Measure P-measure;Multiple linear regression models;Regression model;Ridge regression |
| Date: | 2009 |
| Issue Date: | 2010-04-08 12:06:02 (UTC+0) |
| Publisher: | Asia University |
| Abstract: | The well known fuzzy measures, λ-measure and P-measure, have only one formulaic solution, the former is not a closed form, and the later is not sensitive. An improved multivalent fuzzy measure with infinitely many solutions of closed form, called L-measure, is proposed by our previous work. In this paper, expend the L-measure for being more choice, and get an improved fuzzy measures, called "hth-order L-measure", denoted as L <sup>h</sup>-measure , and a new Choquet integral regression model based on this L<sup>h</sup>-measure is also proposed. For evaluating the proposed regression models with different fuzzy measures, a real data experiment by using a 5-fold cross-validation mean square error (MSE) is conducted. The performances of Choquet integral regression models with fuzzy measure based on ?-measure, P-measure, L-measure and L<sup>h</sup>-measure, respectively, a ridge regression model, and a multiple linear regression model are compared. Experimental result shows that the Choquet integral regression models with L<sup>h</sup>-measure based on support outperforms others forecasting models. © 2009 IEEE. |
| Relation: | Proceedings of the 2009 International Conference on Machine Learning and Cybernetics 6:3177-3182 |
| Appears in Collections: | [生物資訊與醫學工程學系 ] 會議論文
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