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Information Technology Journal
  Year: 2010 | Volume: 9 | Issue: 4 | Page No.: 652-658
DOI: 10.3923/itj.2010.652.658
 
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Finding an Optimal Combination of Key Training Items Using Genetic Algorithms and Support Vector Machines
Chien-Che Huang, Ruey-Gwo Chung, Rong-Chang Chen, Tung-Shou Chen, Tzu-Ning Le, Chih-Jung Hsu and Ying-Chih Tsai

Abstract: The purpose of this study was to find a best combination of key training items. Companies are generally concerned about whether training can increase business performance and want to know what training items are crucial to enhancement of performance. Thus, there is a need to find the key training items. In this study, a combined scheme of Genetic Algorithms (GA) and Support Vector Machines (SVM) is employed to find the optimal combination of the key items. The data used are collected from some small and medium-sized enterprises and are from the database of the Bureau of Employment and Vocational Training (BEVT) in Taiwan. Results from this study show that an optimal combination of key items can be effectively found by using the proposed approach. When companies intend to successfully improve the business performance and cost-efficiently implement training, they can focus on the key training items.
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How to cite this article:

Chien-Che Huang, Ruey-Gwo Chung, Rong-Chang Chen, Tung-Shou Chen, Tzu-Ning Le, Chih-Jung Hsu and Ying-Chih Tsai, 2010. Finding an Optimal Combination of Key Training Items Using Genetic Algorithms and Support Vector Machines. Information Technology Journal, 9: 652-658.

DOI: 10.3923/itj.2010.652.658

URL: https://scialert.net/abstract/?doi=itj.2010.652.658
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