NDLI: A Comparative Study of Threshold-Based Feature Selection Techniques

Content Provider	IEEE Xplore Digital Library
Author	Huanjing Wang Khoshgoftaar, T.M. Van Hulse, J.
Copyright Year	2010
Abstract	Given high-dimensional software measurement data, researchers and practitioners often use feature (metric) selection techniques to improve the performance of software quality classification models. This paper presents our newly proposed threshold-based feature selection techniques, comparing the performance of these techniques by building classification models using five commonly used classifiers. In order to evaluate the effectiveness of different feature selection techniques, the models are evaluated using eight different performance metrics separately since a given performance metric usually captures only one aspect of the classification performance. All experiments are conducted on three Eclipse data sets with different levels of class imbalance. The experiments demonstrate that the choice of a performance metric may significantly influence the results. In this study, we have found four distinct patterns when utilizing eight performance metrics to order 11 threshold-based feature selection techniques. Moreover, performances of the software quality models either improve or remain unchanged despite the removal of over 96% of the software metrics (attributes).
Starting Page	499
Ending Page	504
File Size	320454
Page Count	6
File Format	PDF
ISBN	9781424479641
DOI	10.1109/GrC.2010.104
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Software metrics performance metrics Training data Data models Software classification Analysis of variance threshold-based feature selection technique software metrics
Content Type	Text
Resource Type	Article

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