Measuring robustness of Feature Selection techniques on software engineering datasets

Feature Selection is a process which identifies irrelevant and redundant features from a high-dimensional dataset (that is, a dataset with many features), and removes these before further analysis is performed. Recently, the robustness (e.g., stability) of feature selection techniques has been studi...

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Bibliographic Details
Published in:2011 IEEE International Conference on Information Reuse and Integration pp. 309 - 314
Main Authors: Huanjing Wang, Khoshgoftaar, T. M., Wald, R.
Format: Conference Proceeding
Language:English
Published: IEEE 01.08.2011
Subjects:
Analysis of variance
fault-prone program module
Indexes
Radio frequency
Robustness
Robustness of feature selection
Software
software metrics
software quality classification
Stability criteria
ISBN:9781457709647, 1457709643
Online Access:Get full text
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Summary:Feature Selection is a process which identifies irrelevant and redundant features from a high-dimensional dataset (that is, a dataset with many features), and removes these before further analysis is performed. Recently, the robustness (e.g., stability) of feature selection techniques has been studied, to examine the sensitivity of these techniques to changes in their input data. In this study, we investigate the robustness of six commonly used feature selection techniques as the magnitude of change to the datasets and the size of the selected feature subsets are varied. All experiments were conducted on 16 datasets from three real-world software projects. The experimental results demonstrate that Gain Ratio shows the least stability on average while two different versions of ReliefF show the most stability. Results also show that making smaller changes to the datasets has less impact on the stability of feature ranking techniques applied to those datasets.
ISBN:9781457709647
1457709643
DOI:10.1109/IRI.2011.6009565