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Combining feature selection with decision tree criteria and neural network for corporate value classification

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Abstract

This study aims to classify corporate values among Japanese companies based on their corporate social responsibility (CSR) performances. Since there are many attributes in CSR, feature selection with decision tree criteria is used to select the attributes that can classify corporate values. The feature selection found that 41% of 37 total attributes, or only 15 attributes, are needed to classify corporate values. The accuracy of building the tree used to find the 15 attributes is low. To increase the accuracy, the attributes are trained in a neural network. The accuracy of the decision tree is 0.7, and the accuracy of the neural for training the 15 attributes increased to 0.75. To sum up, this study found, companies with higher corporate values seek to enhance their CSR activities or to empower secondary stakeholders. In contrast, companies with low corporate values still focus their CSR activities on primary stakeholders.

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Hidayati, R., Kanamori, K., Feng, L., & Ohwada, H. (2016). Combining feature selection with decision tree criteria and neural network for corporate value classification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9806 LNCS, pp. 31–42). Springer Verlag. https://doi.org/10.1007/978-3-319-42706-5_3

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