An empirical comparison of nine pattern classifiers

Quoc Long Tran; Kar Ann Toh; Dipti Srinivasan; Kok Leong Wong; Shaun Qiu Cen Low

doi:10.1109/TSMCB.2005.847745

An empirical comparison of nine pattern classifiers

Quoc Long Tran, Kar Ann Toh, Dipti Srinivasan, Kok Leong Wong, Shaun Qiu Cen Low

Department of Electrical and Electronic Engineering

Research output: Contribution to journal › Article

15 Citations (Scopus)

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.

Original language	English
Pages (from-to)	1079-1091
Number of pages	13
Journal	IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume	35
Issue number	5
DOIs	https://doi.org/10.1109/TSMCB.2005.847745
Publication status	Published - 2005 Oct 1

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Software
Information Systems
Human-Computer Interaction
Computer Science Applications
Electrical and Electronic Engineering

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10.1109/TSMCB.2005.847745

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Tran, Q. L., Toh, K. A., Srinivasan, D., Wong, K. L., & Low, S. Q. C. (2005). An empirical comparison of nine pattern classifiers. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 35(5), 1079-1091. https://doi.org/10.1109/TSMCB.2005.847745

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