A discretization algorithm based on Class-Attribute Contingency Coefficient

Cheng Jung Tsai, Chien I. Lee, Wei Pang Yang

研究成果: Article

139 引文 斯高帕斯(Scopus)

摘要

Discretization algorithms have played an important role in data mining and knowledge discovery. They not only produce a concise summarization of continuous attributes to help the experts understand the data more easily, but also make learning more accurate and faster. In this paper, we propose a static, global, incremental, supervised and top-down discretization algorithm based on Class-Attribute Contingency Coefficient. Empirical evaluation of seven discretization algorithms on 13 real datasets and four artificial datasets showed that the proposed algorithm could generate a better discretization scheme that improved the accuracy of classification. As to the execution time of discretization, the number of generated rules, and the training time of C5.0, our approach also achieved promising results.

原文English
頁(從 - 到)714-731
頁數18
期刊Information Sciences
178
發行號3
DOIs
出版狀態Published - 2008 二月 1

    指紋

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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