An empirical comparison of nine pattern classifiers.
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.
Tran
Quoc-Long
QL
Toh
Kar-Ann
KA
Srinivasan
Dipti
D
Wong
Kok-Leong
KL
Low
Shaun Qiu-Cen
SQ
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012984:Software; D012986:Software Validation