Multivariate Regression Forest for Categorical Attribute Data
Aiming at the problem that regression models such as linear regression, SVR and most multivariate regression trees cannot directly use classification attributes for regression analysis, a decision tree node division method that can combine multiple types of attributes is proposed. The center of the...
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Published in | Ji suan ji ke xue Vol. 49; no. 1; pp. 108 - 114 |
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Main Authors | , |
Format | Journal Article |
Language | Chinese |
Published |
Chongqing
Guojia Kexue Jishu Bu
01.01.2022
Editorial office of Computer Science |
Subjects | |
Online Access | Get full text |
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Summary: | Aiming at the problem that regression models such as linear regression, SVR and most multivariate regression trees cannot directly use classification attributes for regression analysis, a decision tree node division method that can combine multiple types of attributes is proposed. The center of the set on the classification attribute and the distance from the sample to the center, so that the classification attribute can also participate in the clustering process of the sample like the numerical attribute, so as to form the division of the sample set. The decision tree selects an appropriate ensemble scheme, and the resulting ensemble is called Cluster Regression Forest (CRF). Finally, the regression mean absolute error (MAE) of CRF and other 9 regression models is compared on 12 UCI public datasets and root mean square error (RMSE), the experimental results show that CRF has the best performance among the 10 regression models. |
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ISSN: | 1002-137X |
DOI: | 10.11896/jsjkx.201200189 |