A Study of Improving the Performance of Mining Multi-Valued and Multi-Labeled Data

Article type: Research Article

Authors: Tsai, Cheng-Jung^;

Affiliations: Institute of Statistics and Information Science, National Changhua University of Education, Changhua, Taiwan, R.O.C, e-mail: [email protected]

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Abstract: Nowadays data mining algorithms are successfully applying to analyze the real data in our life to provide useful suggestion. Since some available real data is multi-valued and multi-labeled, researchers have focused their attention on developing approaches to mine multi-valued and multi-labeled data in recent years. Unfortunately, there are no algorithms can discretize multi-valued and multi-labeled data to improve the performance of data mining. In this paper, we proposed a novel approach to solve this problem. Our approach is based on a statistical-based discretization metric and the simulated annealing search algorithm. Experimental results show that our approach can effectively improve the performance of the-state-of-art multi-valued and multi-labeled classification algorithm.

Keywords: data mining, classification, multi-valued, multi-labeled, discretization, simulated annealing search

Journal: Informatica, vol. 25, no. 1, pp. 95-111, 2014