Improvement of Classification Accuracy on Success and Failure Factors in Software Reuse using Feature Selection


KIPS Transactions on Software and Data Engineering, Vol. 2, No. 4, pp. 219-226, Apr. 2013
10.3745/KTSDE.2013.2.4.219,   PDF Download:

Abstract

Feature selection is the one of important issues in the field of machine learning and pattern recognition. It is the technique to find a subset from the source data and can give the best classification performance. Ie, it is the technique to extract the subset closely related to the purpose of the classification. In this paper, we experimented to select the best feature subset for improving classification accuracy when classify success and failure factors in software reuse. And we compared with existing studies. As a result, we found that a feature subset was selected in this study showed the better classification accuracy.


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Cite this article
[IEEE Style]
Y. O. Kim and K. T. Kwon, "Improvement of Classification Accuracy on Success and Failure Factors in Software Reuse using Feature Selection," KIPS Transactions on Software and Data Engineering, vol. 2, no. 4, pp. 219-226, 2013. DOI: 10.3745/KTSDE.2013.2.4.219.

[ACM Style]
Young Ok Kim and Ki Tae Kwon. 2013. Improvement of Classification Accuracy on Success and Failure Factors in Software Reuse using Feature Selection. KIPS Transactions on Software and Data Engineering, 2, 4, (2013), 219-226. DOI: 10.3745/KTSDE.2013.2.4.219.