Research on multi-valued and multi-labeled decision trees Conference Paper uri icon

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. Springer-Verlag Berlin Heidelberg 2006.

name of conference

  • Advanced Data Mining and Applications, Second International Conference, ADMA 2006, Xi'an, China, August 14-16, 2006, Proceedings

published proceedings

  • ADVANCED DATA MINING AND APPLICATIONS, PROCEEDINGS

author list (cited authors)

  • Li, H., Zhao, R., Chen, J., & Xiang, Y.

complete list of authors

  • Li, Hong||Zhao, Rui||Chen, Jianer||Xiang, Yao

publication date

  • January 2006