Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICIEA.2006.257073
Title: A framework for empirical classifiers comparison
Authors: Abdullah, M.R.B.
Toh, K.-A.
Srinivasan, D. 
Issue Date: 2006
Source: Abdullah, M.R.B.,Toh, K.-A.,Srinivasan, D. (2006). A framework for empirical classifiers comparison. 2006 1st IEEE Conference on Industrial Electronics and Applications : -. ScholarBank@NUS Repository. https://doi.org/10.1109/ICIEA.2006.257073
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. © 2006 IEEE.
Source Title: 2006 1st IEEE Conference on Industrial Electronics and Applications
URI: http://scholarbank.nus.edu.sg/handle/10635/68808
ISBN: 078039514X
DOI: 10.1109/ICIEA.2006.257073
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