A study of improving the performance of mining multi-valued and multi-labeled data

Research output: Contribution to journalArticle


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.

Original languageEnglish
Pages (from-to)95-111
Number of pages17
JournalInformatica (Netherlands)
Issue number1
Publication statusPublished - 2014 Jan 25


All Science Journal Classification (ASJC) codes

  • Information Systems
  • Applied Mathematics

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