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 | |
| Publication status | Published - 2005 Oct 1 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Artificial Intelligence
- Human-Computer Interaction
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An empirical comparison of nine pattern classifiers. / Tran, Quoc Long; Toh, Kar Ann; Srinivasan, Dipti; Wong, Kok Leong; Low, Shaun Qiu Cen.
In: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 35, No. 5, 01.10.2005, p. 1079-1091.Research output: Contribution to journal › Article
TY - JOUR
T1 - An empirical comparison of nine pattern classifiers
AU - Tran, Quoc Long
AU - Toh, Kar Ann
AU - Srinivasan, Dipti
AU - Wong, Kok Leong
AU - Low, Shaun Qiu Cen
PY - 2005/10/1
Y1 - 2005/10/1
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=26844504817&partnerID=8YFLogxK
U2 - 10.1109/TSMCB.2005.847745
DO - 10.1109/TSMCB.2005.847745
M3 - Article
VL - 35
SP - 1079
EP - 1091
JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
SN - 1083-4419
IS - 5
ER -