MMDT: a multi-valued and multi-labeled decision tree classifier for data mining
We have proposed a decision tree classifier named MMC (multi-valued and multi-labeled classifier) before. MMC is known as its capability of classifying a large multi-valued and multi-labeled data. Aiming to improve the accuracy of MMC, this paper has developed another classifier named MMDT (multi-va...
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Published in | Expert systems with applications Vol. 28; no. 4; pp. 799 - 812 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Elsevier Ltd
01.05.2005
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Subjects | |
Online Access | Get full text |
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Summary: | We have proposed a decision tree classifier named
MMC (multi-valued and multi-labeled classifier) before. MMC is known as its capability of classifying a large multi-valued and multi-labeled data. Aiming to improve the accuracy of MMC, this paper has developed another classifier named
MMDT (multi-valued and multi-labeled decision tree). MMDT differs from MMC mainly in attribute selection. MMC attempts to split a node into child nodes whose records approach the same multiple labels. It basically measures the average similarity of labels of each child node to determine the goodness of each splitting attribute. MMDT, in contrast, uses another measuring strategy which considers not only the average similarity of labels of each child node but also the average appropriateness of labels of each child node. The new measuring strategy takes scoring approach to have a look-ahead measure of accuracy contribution of each attribute's splitting. The experimental results show that MMDT has improved the accuracy of MMC. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
ISSN: | 0957-4174 1873-6793 |
DOI: | 10.1016/j.eswa.2004.12.035 |