Applying a complexity-based Choquet integral to evaluate students' performance

Jiunn I. Shieh, Hsin Hung Wu, Hsiang Chuan Liu

研究成果: Article同行評審

34 引文 斯高帕斯(Scopus)

摘要

The weighted arithmetic mean and the regression methods are the most often used operators to aggregate criteria in decision making problems with the assumption that there are no interactions among criteria. When interactions among criteria exist, the discrete Choquet integral is proved to be an adequate aggregation operator by further taking into accounts the interactions. In this study, we propose a complexity-based method to construct fuzzy measures needed by the discrete Choquet integral and a real data set is analyzed. The advantage of the complexity-based method is that no population probability is to be estimated such that the error of estimating the population probability is reduced. Four methods, including weighted arithmetic method, regression-based method, the discrete Choquet integral with the entropy-based method, and our proposed discrete Choquet integral with the complexity-based method, are used in this study to evaluate the students' performance based on a Basic Competence Test. The results show that the students' overall performance evaluated by our proposed discrete Choquet integral with the complexity-based method is the best among the four methods when the interactions among criteria exist.

原文English
頁(從 - 到)5100-5106
頁數7
期刊Expert Systems with Applications
36
發行號3 PART 1
DOIs
出版狀態Published - 2009 四月

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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