ISBN: 978-981-11-3671-9 DOI: 10.18178/wcse.2017.06.032
Redundancy Weighting for Software Effort Estimation with Case Based Reasoning
Abstract— Case-based reasoning (CBR) is a widely used approach in software effort estimation (SEE).
Unfortunately, it may be over fed by redundant feature(s) that may lead to erroneous prediction. To alleviate
the problem, this paper proposes a Relevance-Redundancy (R2D) distance that incorporates redundancy
weighting. Experiment results demonstrate that R2D achieves optimal MAR and Pred(25) on 4 benchmark
datasets with an average improvement of 17.4%and 27.8% against second optimal methods.
Index Terms— feature weighting, redundancy, mutual information, case based reasoning
Qin Liu, Hongming Zhu
School of Software Engineering, Tongji University, CHINA
Department of Computer Science and Technology, Tongji University
Cite: Qin Liu, Jiakai Xiao, Hongming Zhu, "Redundancy Weighting for Software Effort Estimation with Case Based Reasoning," Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering, pp. 181-187, Beijing, 25-27 June, 2017.