A framework for empirical classifiers comparison

Mohammad Ridzuan Bin Abdullah, Kar Ann Toh, Dipti Srinivasan

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

4 Citations (Scopus)

Abstract

In this paper, we seek to establish a framework for empirical comparison of performance of pattern classifiers, allowing comparisons to be made consistently across different studies. As many as 106 datasets from the University of California, Irvine, Machine Learning Repository were used as comparison benchmarks. The framework provides a clear definition of the experimental setup so that it can be unambiguously reproduced or verified by others. Multiple runs of cross-validation and tuning were employed to minimize the possibility of random effects causing much biases in the results obtained. The metrics used to compare among different classifiers are based solely on simple readings obtained through classification tests. This allows future comparisons to be made readily adaptable for inclusion of new metrics.

Original languageEnglish
Title of host publication2006 1st IEEE Conference on Industrial Electronics and Applications
DOIs
Publication statusPublished - 2006 Dec 1
Event2006 1st IEEE Conference on Industrial Electronics and Applications, ICIEA 2006 - Singapore, Singapore
Duration: 2006 May 242006 May 26

Publication series

Name2006 1st IEEE Conference on Industrial Electronics and Applications

Other

Other2006 1st IEEE Conference on Industrial Electronics and Applications, ICIEA 2006
CountrySingapore
CitySingapore
Period06/5/2406/5/26

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering

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  • Cite this

    Abdullah, M. R. B., Toh, K. A., & Srinivasan, D. (2006). A framework for empirical classifiers comparison. In 2006 1st IEEE Conference on Industrial Electronics and Applications [4026002] (2006 1st IEEE Conference on Industrial Electronics and Applications). https://doi.org/10.1109/ICIEA.2006.257073