@InProceedings{MohammadaliBagheri2012,
author="Mohammad ali Bagheri
and Qigang Gao
and Sergio Escalera",
title="Efficient pairwise classification using Local Cross Off strategy",
booktitle="25th Canadian Conference on Artificial Intelligence",
year="2012",
volume="7310",
pages="25--36",
abstract="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.",
optnote="HuPBA;MILAB",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2044), last updated on Thu, 13 Mar 2014 10:19:12 +0100",
isbn="978-3-642-30352-4",
issn="0302-9743",
doi="10.1007/978-3-642-30353-1_3",
file=":http://refbase.cvc.uab.es/files/BGE2012c.pdf:PDF"
}