Please use this identifier to cite or link to this item: https://doi.org/10.1109/IEEM.2008.4738130
Title: Optimization of feature weights and number of neighbors for analogy based cost estimation in software project management
Authors: Li, Y.F.
Xie, M. 
Goh, T.N. 
Keywords: Analogy based estimation
Feature weights
Genetic algorithm
K-nearest neighbors
Software cost estimation
Software project management
Issue Date: 2008
Source: Li, Y.F., Xie, M., Goh, T.N. (2008). Optimization of feature weights and number of neighbors for analogy based cost estimation in software project management. 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008 : 1542-1546. ScholarBank@NUS Repository. https://doi.org/10.1109/IEEM.2008.4738130
Abstract: Software cost estimation affects almost all activities of software project development such as: biding, planning, and budgeting, thus it is very crucial to the success of software project management. In past decades, many methods have been proposed for cost estimation. Analogy Based cost Estimation (ABE) is among the most popular techniques due to its conceptual simplicity and empirical competitiveness. In order to improve ABE model, many previous studies have focused on optimizing the feature weights in the similarity function. However, according to some prior studies, the K parameter for the K-nearest neighbor is also essential to the performance of ABE. Nevertheless, few studies attempt to optimize the K number of neighbors and most of them are based on the trial-error scheme. In this study, we propose the Genetic Algorithm to simultaneously optimize the K parameter and the feature weights for ABE (OKFWSABE). The proposed OKFWABE method is validated on three real-world software engineering data sets. The experiment results show that our methods could significantly improve the prediction accuracy of conventional ABE and has the potential to become an effective method for software cost estimation. © 2008 IEEE.
Source Title: 2008 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2008
URI: http://scholarbank.nus.edu.sg/handle/10635/72366
ISBN: 9781424426300
DOI: 10.1109/IEEM.2008.4738130
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