Handling class imbalance problem in cultural modeling

Su Peng, Mao Wenji, Daniel Zeng, Li Xiaochen, Fei Yue Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Cultural modeling is an emergent and promising research area in social computing. It aims at developing behavioral models of groups and analyzing the impact of culture factors on group behavior using computational methods. Machine learning methods in particular classification, play a central role in such applications. In cultural modeling, it is expected that classifiers yield good performance. However, the performance of standard classifiers is often severely hindered in practice due to the imbalanced distribution of class in cultural data. In this paper, we identify class imbalance problem in cultural modeling domain. To handle the problem, we propose a user involved solution employing the receiver operating characteristic (ROC) analysis for classification algorithms with sampling approaches. Finally, we conduct experiment to verify the effectiveness of the proposed solution.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Intelligence and Security Informatics, ISI 2009
Pages251-256
Number of pages6
DOIs
StatePublished - 2009
Event2009 IEEE International Conference on Intelligence and Security Informatics, ISI 2009 - Dallas, TX, United States
Duration: Jun 8 2009Jun 11 2009

Publication series

Name2009 IEEE International Conference on Intelligence and Security Informatics, ISI 2009

Other

Other2009 IEEE International Conference on Intelligence and Security Informatics, ISI 2009
CountryUnited States
CityDallas, TX
Period6/8/096/11/09

Keywords

  • Class imbalance problem
  • Classification
  • Cultural modeling
  • ROC analysis
  • Sampling

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Software

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