Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCB.2005.847745
Title: An empirical comparison of nine pattern classifiers
Authors: Tran, Q.-L.
Toh, K.-A.
Srinivasan, D. 
Wong, K.-L.
Low, S.Q.-C.
Keywords: Hyperbolic functions
Machine learning
Parameter estimation
Pattern classification
Polynomials
Issue Date: Oct-2005
Source: Tran, Q.-L., Toh, K.-A., Srinivasan, D., Wong, K.-L., Low, S.Q.-C. (2005-10). An empirical comparison of nine pattern classifiers. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 35 (5) : 1079-1091. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCB.2005.847745
Abstract: There are many learning algorithms available in the field of pattern classification and people are still discovering new algorithms that they hope will work better. Any new learning algorithm, beside its theoretical foundation, needs to be justified in many aspects including accuracy and efficiency when applied to real life problems. In this paper, we report the empirical comparison of a recent algorithm RM, its new extensions and three classical classifiers in different aspects including classification accuracy, computational time and storage requirement. The comparison is performed in a standardized way and we believe that this would give a good insight into the algorithm RM and its extension. The experiments also show that nominal attributes do have an impact on the performance of those compared learning algorithms. © 2005 IEEE.
Source Title: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
URI: http://scholarbank.nus.edu.sg/handle/10635/55011
ISSN: 10834419
DOI: 10.1109/TSMCB.2005.847745
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