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2008
ACM

An over-sampling method for analogy-based software effort estimation

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An over-sampling method for analogy-based software effort estimation
This paper proposes a novel method to generate synthetic project cases and add them to a fit dataset for the purpose of improving the performance of analogy-based software effort estimation. The proposed method extends conventional over-sampling method, which is a preprocessing procedure for n-group classification problems, which makes it suitable for any imbalanced dataset to be used in analogy-based system. We experimentally evaluated the effect of the over-sampling method to improve the performance of the analogy-based software effort estimation by using the Desharnais dataset. Results show significant improvement to the estimation accuracy by using our approach. Categories and Subject Descriptors D.2.9 [Software Engineering]: Management
Yasutaka Kamei, Jacky Keung, Akito Monden, Ken-ich
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2008
Where ESEM
Authors Yasutaka Kamei, Jacky Keung, Akito Monden, Ken-ichi Matsumoto
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