title: An empirical comparison of nine pattern classifiers.
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.
authors.forename: Quoc-Long
authors.initials: QL
authors.lastname: Tran
authors.forename: Kar-Ann
authors.initials: KA
authors.lastname: Toh
authors.forename: Dipti
authors.initials: D
authors.lastname: Srinivasan
authors.forename: Kok-Leong
authors.initials: KL
authors.lastname: Wong
authors.forename: Shaun Qiu-Cen
authors.initials: SQ
authors.lastname: Low
mesh_terms: 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