A Comparative Study of Ensemble Feature Selection Techniques for Software Defect Prediction

Feature selection has become the essential step in many data mining applications. Using a single feature subset selection method may generate local optima. Ensembles of feature selection methods attempt to combine multiple feature selection methods instead of using a single one. We present a compreh...

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Bibliographic Details
Published in2010 International Conference on Machine Learning and Applications pp. 135 - 140
Main Authors Huanjing Wang, Khoshgoftaar, T M, Napolitano, A
Format Conference Proceeding
LanguageEnglish
Japanese
Published IEEE 01.12.2010
Subjects
Analysis of variance
Data models
defect prediction
ensembles
feature ranking
Frequency modulation
Measurement
Radio frequency
Software
Training data
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Summary:Feature selection has become the essential step in many data mining applications. Using a single feature subset selection method may generate local optima. Ensembles of feature selection methods attempt to combine multiple feature selection methods instead of using a single one. We present a comprehensive empirical study examining 17 different ensembles of feature ranking techniques (rankers) including six commonly-used feature ranking techniques, the signal-to-noise filter technique, and 11 threshold-based feature ranking techniques. This study utilized 16 real-world software measurement data sets of different sizes and built 13,600 classification models. Experimental results indicate that ensembles of very few rankers are very effective and even better than ensembles of many or all rankers.
ISBN:1424492114
9781424492114
DOI:10.1109/ICMLA.2010.27