Research on multi-valued and multi-labeled decision trees

Authors:
Hong Li;Rui Zhao;Jianer Chen;Yao Xiang
Affiliations:
School of Information Science and Engineering, Central South University, Changsha, China;School of Information Science and Engineering, Central South University, Changsha, China;Department of Computer Science, Texas A&M University, Texas;School of Information Science and Engineering, Central South University, Changsha, China
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

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Abstract

Ordinary decision tree classifiers are used to classify data with single-valued attributes and single-class labels. This paper develops a new decision tree classifier SSC for multi-valued and multi-labeled data, on the basis of the algorithm MMDT, improves on the core formula for measuring the similarity of label-sets, which is the essential index in determining the goodness of splitting attributes, and proposes a new approach of measuring similarity considering both same and consistent features of label-sets, and together with a dynamic approach of adjusting the calculation proportion of the two features according to current data set. SSC makes the similarity of label-sets measured more comprehensive and accurate. The empirical results prove that SSC indeed improves the accuracy of MMDT, and has better classification efficiency.