文章基本信息

标题：CLASSIFICATION MODELS BASED FORWARD SELECTION FOR BUSINESS PERFORMANCE PREDICTION
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作者：EDI NOERSASONGKO ; PURWANTO ; GURUH FAJAR SHIDIK 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2014
卷号：67
期号：2
出版社：Journal of Theoretical and Applied
摘要：This paper proposes a classification model to improve the accuracy of prediction for business performance. The proposed model uses a combination of forward selection method to select the optimum attributes and classification models. Business performance data set is used to evaluate the accuracy of the proposed model. From results of experiments show that the combination of forward selection and Na�ve Bayes model can improve the prediction accuracy of business performance compared to the other classification models, namely Logistic Regression, k-NN, Na�ve Bayes, C4.5 and Support Vector Machine models significantly. The proposed model also yields better result compared to the other attribute selection using backward elimination method.
关键词：Forward Selection; Na�ve Bayes; Entrepreneur; Business performance; Classification Models