Statistical classification methods such as multivariate discriminant analysis have been widely used in bond rating classification in spite of the limitations of the methodology. Recently, neural networks have emerged as new methods for business classification. This approach to neural networks training is to categorize a new instance as one of the predefined bond classes. Such a conventional approach has limitations in dealing with the ordinal nature of bond rating. In addition, most of the prior studies have used sample data which are evenly divided among the classes. However, the natural population in real application is usually unevenly divided among the classes. Under such circumstances, it is hard to achieve good predictive performance. As the number of classes to be recognized increases, the predictive performance decreases. In this article, to increase the predictive performance in real‐world bond rating, we propose the ordinal pairwise partitioning (OPP) approach to backpropagation neural networks training. The main idea of the OPP approach is to partition the data set in the ordinal and pairwise manner according to the output classes. Then, each backpropagation neural networks model is trained by using each partitioned data set and is separately used for classification. Experimental results show that the predictive performance of the proposed OPP approach can be significantly enhanced, when compared to the conventional neural networks modeling approach as well as multivariate discriminant analysis. The OPP approach has two computation methods, and we discuss under which circumstances one method performs better than the other. We also show the generalizability of the OPP approach. © 1997 by John Wiley & Sons, Ltd.

Due to increasing concerns about environmental protection, the environmental sustainability of businesses has been widely considered in the manufacturing and supply chain context. Further, its adoption has been implemented in the retail industry for marketing field, including green product promotion. This study aimed to propose a customer purchasing behavior analysis as an alternative for supporting decision-making in order to promote green products in retail stores. Hence, right-on-target marketing strategies can be implemented appropriately. The study was carried out using shopping path data collected by radio frequency identification (RFID) from a large retail store in Seoul, South Korea. In addition, the store layout and its traffic were also analyzed. This method is expected to help experts providing appropriate decision alternatives. In addition, it can help retailers in order to increase product sales and achieve high levels of customer satisfaction.

Statistical classification methods such as multivariate discriminant analysis have been widely used in bond rating classification in spite of the limitations of the methodology. Recently, neural networks have emerged as new methods for business classification. This approach to neural networks training is to categorize a new instance as one of the predefined bond classes. Such a conventional approach has limitations in dealing with the ordinal nature of bond rating. In addition, most of the prior studies have used sample data which are evenly divided among the classes. However, the natural population in real application is usually unevenly divided among the classes. Under such circumstances, it is hard to achieve good predictive performance. As the number of classes to be recognized increases, the predictive performance decreases. In this article, to increase the predictive performance in real-world bond rating, we propose the ordinal pairwise partitioning (OPP) approach to backpropagation neural networks training. The main idea of the OPP approach is to partition the data set in the ordinal and pairwise manner according to the output classes. Then, each backpropagation neural networks model is trained by using each partitioned data set and is separately used for classification. Experimental results show that the predictive performance of the proposed OPP approach can be significantly enhanced, when compared to the conventional neural networks modeling approach as well as multivariate discriminant analysis. The OPP approach has two computation methods, and we discuss under which circumstances one method performs better than the other. We also show the generalizability of the OPP approach.

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