期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2012
卷号：9
期号：3
出版社：IJCSI Press
摘要：Artificial Bee Colony (ABC) is a popular meta-heuristic search algorithm used in solving numerous combinatorial optimization problems. Feature Selection (FS) helps to speed up the process of classification by extracting the relevant and useful information from the dataset. FS is seen as an optimization problem because selecting the appropriate feature subset is very important. Classifier Ensemble is the best solution for the pitfall of accuracy lag in a single classifier. This paper proposes a novel hybrid algorithm ABCE - the combination of ABC algorithm and a classifier ensemble (CE). A classifier ensemble consisting of Support Vector Machine (SVM), Decision Tree and Nave Bayes, performs the task of classification and ABCE is used as a feature selector to select the most informative features as well as to increase the overall classification accuracy of the classifier ensemble. Ten UCI (University of California, Irvine) benchmark datasets have been used for the evaluation of the proposed algorithm. Three ensembles ABC-CE, ABC-Bagging and ABC-Boosting have been constructed from the finally selected feature subsets. From the experimental results, it can be seen that these ensembles have shown up to 12% increase in the classification accuracy compared to the constituent classifiers and the standard ensembles Bagging, Boosting, ACO-Bagging and ACO-Boosting.
