Journal of Computer Science | |
A Novel Ensemble Method for Regression via Classification Problems | Science Publications | |
Amir Ahmad1  Sami M. Halawani1  Ibrahim A. Albidewi1  | |
关键词: Regression via Classification (RvC); ERD ensembles; classification problem; decision trees; Extreme Randomized Discretization (ERD); Monothetic Contrast Criteria (MCC); RvC perform; Mean Square Error (MSE); neural network; | |
DOI : 10.3844/jcssp.2011.387.393 | |
学科分类:计算机科学(综合) | |
来源: Science Publications | |
【 摘 要 】
Problem statement: Regression via Classification (RvC) is a method in which a regressionproblem is converted into a classification problem. A discretization process is used to covertcontinuous target value to classes. The discretized data can be used with classifiers as a classificationproblem. Approach: In this study, we use a discretization method, Extreme RandomizedDiscretization (ERD), in which bin boundaries are created randomly to create ensembles. Results: Weshow that the proposed ensemble method is useful for RvC problems. We show theoretically that theproposed ensembles for RvC perform better than RvC with the equal-width discretization method. Wealso show the superiority of the proposed ensemble method experimentally. Experimental resultssuggest that the proposed ensembles perform competitively to the method developed specifically forregression problems. Conclusion: As the proposed method is independent of the choice of theclassifier, various classifiers can be used with the proposed method to solve the regression method.
【 授权许可】
Unknown
【 预 览 】
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RO201911300282479ZK.pdf | 70KB | download |