TY - GEN
T1 - Efficient pairwise classification using local cross off strategy
AU - Bagheri, Mohammad Ali
AU - Gao, Qigang
AU - Escalera, Sergio
PY - 2012
Y1 - 2012
N2 - The pairwise classification approach tends to perform better than other well-known approaches when dealing with multiclass classification problems. In the pairwise approach, however, the nuisance votes of many irrelevant classifiers may result in a wrong prediction class. To overcome this problem, a novel method, Local Crossing Off (LCO), is presented and evaluated in this paper. The proposed LCO system takes advantage of nearest neighbor classification algorithm because of its simplicity and speed, as well as the strength of other two powerful binary classifiers to discriminate between two classes. This paper provides a set of experimental results on 20 datasets using two base learners: Neural Networks and Support Vector Machines. The results show that the proposed technique not only achieves better classification accuracy, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes.
AB - The pairwise classification approach tends to perform better than other well-known approaches when dealing with multiclass classification problems. In the pairwise approach, however, the nuisance votes of many irrelevant classifiers may result in a wrong prediction class. To overcome this problem, a novel method, Local Crossing Off (LCO), is presented and evaluated in this paper. The proposed LCO system takes advantage of nearest neighbor classification algorithm because of its simplicity and speed, as well as the strength of other two powerful binary classifiers to discriminate between two classes. This paper provides a set of experimental results on 20 datasets using two base learners: Neural Networks and Support Vector Machines. The results show that the proposed technique not only achieves better classification accuracy, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes.
KW - Multiclass
KW - Neural Networks
KW - Pairwise classification
KW - Support Vector Machines
UR - http://www.scopus.com/inward/record.url?scp=84861738394&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-30353-1_3
DO - 10.1007/978-3-642-30353-1_3
M3 - Article in proceeding
AN - SCOPUS:84861738394
SN - 9783642303524
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 25
EP - 36
BT - Advances in Artificial Intelligence - 25th Canadian Conference on Artificial Intelligence, Canadian AI 2012, Proceedings
T2 - 25th Canadian Conference on Artificial Intelligence, AI 2012
Y2 - 28 May 2012 through 30 May 2012
ER -