Supervised multi-class classification with adaptive and automatic parameter tuning

Chen Chao, Mei Ling Shyu, Shu Ching Chen

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

4 Scopus citations

Abstract

In this paper, a classification framework is developed to address the issue that empirical determination of the parameters and their values typically makes a classification framework less adaptive and general to different data sets and application domains. Experimental results show that our proposed framework achieves (1) better performance over other comparative supervised classification methods, (2) more robust to imbalanced data sets, and (3) smaller performance variance to different data sets.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Information Reuse and Integration, IRI 2009
Pages433-434
Number of pages2
DOIs
StatePublished - 2009
Event2009 IEEE International Conference on Information Reuse and Integration, IRI 2009 - Las Vegas, NV, United States
Duration: Aug 10 2009Aug 12 2009

Publication series

Name2009 IEEE International Conference on Information Reuse and Integration, IRI 2009

Other

Other2009 IEEE International Conference on Information Reuse and Integration, IRI 2009
Country/TerritoryUnited States
CityLas Vegas, NV
Period8/10/098/12/09

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Information Systems
  • Software

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