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Feature Selection for Highly Imbalanced Software Measurement Data

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Recent Trends in Information Reuse and Integration

Abstract

Many data sets include an overabundance of features (i.e., attributes). However, not all features make the same contributions to the class. Selecting a subset of features that are most relevant to the class attribute is a necessary step to create a more meaningful and usable model [11]. In addition, for a two-group classification problem, class imbalance is frequently encountered [7, 34]. Class imbalance refers to the cases where the examples of one class are significantly outnumbered by the examples of the other class in a data set. For instance, in software quality classification, fault-prone (fp) modules typically are much less common than not-fault-prone (nfp) modules. Traditional classification algorithms attempt to improve classification accuracy without considering the relative significance of the different classes, resulting in a large number of misclassifications from minority class (e.g., fp) to majority class (e.g., nfp). This type of misclassification is extremely severe in some domains such as software quality assurance, implying a lost opportunity to correct a faulty module prior to deployment and operation. A variety of techniques have been proposed to counter the problems associated with class imbalance [30].

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Notes

  1. 1.

    weka (Waikato Environment for Knowledge Analysis) is a popular suite of machine learning software written in Java, developed at the University of Waikato. WEKA is free software available under the GNU General Public License. In this study, all experiments and algorithms were implemented in the weka tool.

  2. 2.

    Full data sets can be found at http://www.st.cs.uni-saarland.de/softevo/bug-data/eclipse/

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Authors and Affiliations

  1. Florida Atlantic University, Boca Raton, FL, 33431, USA

    Taghi M. Khoshgoftaar & Jason Van Hulse

  2. Eastern Connecticut State University, Willimantic, CT, 06226, USA

    Kehan Gao

Authors
  1. Taghi M. Khoshgoftaar
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  2. Kehan Gao
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  3. Jason Van Hulse
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Correspondence to Taghi M. Khoshgoftaar .

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Editors and Affiliations

  1. , Department of Computer Engineering, Tobb University, Sögütözü Caddesi 43, Ankara, 06560, Turkey

    Tansel Özyer

  2. , Department of Electrical Engineering, University of Western Ontario, Building 363, London, N6A 5B9, Ontario, Canada

    Keivan Kianmehr

  3. , Department of Computer Engineering, Tobb University, Söğütözü Caddesi 43, Ankara, 06560, Ankara, Turkey

    Mehmet Tan

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Khoshgoftaar, T.M., Gao, K., Van Hulse, J. (2012). Feature Selection for Highly Imbalanced Software Measurement Data. In: Özyer, T., Kianmehr, K., Tan, M. (eds) Recent Trends in Information Reuse and Integration. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0738-6_8

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  • DOI: https://doi.org/10.1007/978-3-7091-0738-6_8

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