A novel hybrid classification model of artificial neural networks and multiple linear regression models

作者：Khashei Mehdi^*; Hamadani Ali Zeinal; Bijari Mehdi

来源：Expert Systems with Applications, 2012, 39(3): 2606-2620.

DOI：10.1016/j.eswa.2011.08.116

• Classification
• Pattern recognition
• Artificial neural networks (ANNs)
• Multiple linear regression models (MLR)
• Discriminant analysis (DA)

The classification problem of assigning several observations into different disjoint groups plays an important role in business decision making and many other areas. Developing more accurate and widely applicable classification models has significant implications in these areas. It is the reason that despite of the numerous classification models available, the research for improving the effectiveness of these models has never stopped. Combining several models or using hybrid models has become a common practice in order to overcome the deficiencies of single models and can be an effective way of improving upon their predictive performance, especially when the models in combination are quite different. In this paper, a novel hybridization of artificial neural networks (ANNs) is proposed using multiple linear regression models in order to yield more general and more accurate model than traditional artificial neural networks for solving classification problems. Empirical results indicate that the proposed hybrid model exhibits effectively improved classification accuracy in comparison with traditional artificial neural networks and also some other classification models such as linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), K-nearest neighbor (KNN), and support vector machines (SVMs) using benchmark and real-world application data sets. These data sets vary in the number of classes (two versus multiple) and the source of the data (synthetic versus real-world). Therefore, it can be applied as an appropriate alternate approach for solving classification problems, specifically when higher forecasting accuracy is needed.

• 出版日期2012-2-15