Abstract
With the rapidly increasing competitiveness in global market, dynamic alliances and virtual enterprises are becoming essential components of the economy in order to meet the market requirements for quality, responsiveness, and customer satisfaction. Partner selection is a key stage in the formation of a successful virtual enterprise. The process can be considered as a multi-class classification problem. In this paper, The Support Vector Machine (SVM) technique is proposed to perform automated ranking of potential partners. Experimental results indicate that desirable outcome can be obtained by using the SVM method in partner selections. In comparison with other methods in the literatures, the SVM-based method is advantageous in terms of generalization performance and the fitness accuracy with a limited number of training datasets.
This work was supported by grant No. 70171025 of National Science Foundation of China and grant No. 02KJB630001 of Research Project Grant of JiangSu, China.
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Wang, J., Zhong, W., Zhang, J. (2004). Support Vector Machine Approach for Partner Selection of Virtual Enterprises. In: Zhang, J., He, JH., Fu, Y. (eds) Computational and Information Science. CIS 2004. Lecture Notes in Computer Science, vol 3314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30497-5_190
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DOI: https://doi.org/10.1007/978-3-540-30497-5_190
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