期刊名称：International Journal of Electronics Communication and Computer Technology
印刷版ISSN：2249-7838
出版年度：2011
卷号：1
期号：1
页码：1
出版社：International Journal of Electronics Communication and Computer Technology
摘要：This paper proposes an innovative combinational algorithm to improve the performance in multiclass classification domains. Because the more accurate classifier the better performance of classification, the researchers in compu ter communities have been tended to improve the accuracies of classifiers. Although a better performance for classifier is defined the more accurate classifier, but turning to the best classifier is not always the best option to obtain the best quality in classification. It means to reach the best classification there is another alternative to use many inaccurate or w eak classifiers each of them is specialized for a sub-space in the problem space and using their consensus vote as the final classifier. So this paper proposes a heuristic classifier ensemble to improve the performance of classification learning. It is specially deal with multiclass problems w hich their aim is to learn the boundaries of each class from many other classes. Based on the concept of multiclass problems classifiers are divided into two different ca tegories: pairwise classifiers and multiclass classifiers. The aim of a pairwise classifier is to separate one class from another one. Because of pairwise classifiers just train for discrimination between two classes, decision boundaries of them are simpler and more effective than those of multiclass classifiers. The main idea behind the proposed method is to focus classifier in the erroneous spaces of problem and use of pairwise classification concept instead of multiclass classification concept. Indeed although usage of pairwise classification concept instead of multiclass classification concept is not new, w e propose a new pairwise classifier ensemble with a very lower order. In this paper, first the most confused classes are determined and then some ensembles of classifiers are created. The classifiers of each of these ensembles jointly work using majority weighting votes. The results of these ensembles are combined to decide the final vote in a w eighted manner. Finally the outputs of these ensembles are heuristically aggregated. The proposed framew ork is evaluated on a very large scale Persian digit handwritten dataset and the experimental results show the effectiveness of the algorithm
关键词：Genetic Algorithm; Optical Character ;Recognition; Pairwise Classifier; Multiclass Classification
