Enhancing the classification accuracy by scatter-search-based ensemble approach

Data-mining algorithms have been used in many classification problems. Among them, the decision tree (DT), back-propagation network (BPN), and support vector machine (SVM) are popular and can be applied to various areas. Nevertheless, different problems may require different parameter values when applying DT, BPN or SVM. If parameter values are not set well, results may turn out to be unsatisfactory. Further, a dataset may contain many features; however, not all features are beneficial for classifications. Therefore, a scatter search (SS) approach is proposed to obtain the better parameters and select the beneficial subset of features to attain better classification results. The above classification algorithms have their respective advantages and disadvantages, and suitability is influenced by the characteristics of the problem. If the algorithms can function together in a so-called ensemble, it is expected that better results can be obtained. Therefore, this study adapts ensemble to further enhance the classification accuracy rate. In order to evaluate the performance of the proposed approach, datasets in UCI (University of California, Irvine) were applied as the test problem set. The corresponding results were compared to several well-known, published approaches. The comparative study shows that the proposed approach improved the classification accuracy rate in most datasets. Thus, the proposed approach can be useful to both practitioners and researchers. (C) 2010 Elsevier B.V. All rights reserved.