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| A Multi-Valued Attribute and Multi-Labeled Data Decision Tree Algorithm |
| LI Hong, CHEN Song-Qiao, ZHAO Rui, GUO Yue-Jian |
| School of Information Science and Engineering, Central South University, Changsha 410083 |
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Abstract Multi-valued and multi-labeled classifier (MMC) and multi-valued and multi-labeled decision tree (MMDT) are two existing decision tree algorithms for dealing with multi-valued and multi-labeled data . Based on the two algorithms, formula sim3 is put forward to calculate the similarity between two label sets. By amending the measuring formula of samebased similarity of labelsets in MMDT, a new decision tree algorithm, similarity of same and consistent in constructing same in predicting (SCC_SP) is proposed with comprehensive consideration of both similarity and appropriateness of the label set. Results of contrast experiments with the same prediction mechanism show that SCC_SP has higher accuracy rate than MMDT.
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Received: 24 August 2006
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2007, Vol. 20 
