Detecting Fault Modules Applying Feature Selection to Classifiers
- 1 August 2007
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2007 IEEE International Conference on Information Reuse and Integration
- p. 667-672
- https://doi.org/10.1109/iri.2007.4296696
Abstract
At present, automated data collection tools allow us to collect large amounts of information, not without associated problems. This paper, we apply feature selection to several software engineering databases selecting attributes with the final aim that project managers can have a better global vision of the data they manage. In this paper, we make use of attribute selection techniques in different datasets publicly available (PROMISE repository), and different data mining algorithms for classification to defect faulty modules. The results show that in general, smaller datasets with less attributes maintain or improve the prediction capability with less attributes than the original datasets.Keywords
This publication has 7 references indexed in Scilit:
- Finding the Right Data for Software Cost ModelingIEEE Software, 2005
- Feature Selection for Knowledge Discovery and Data MiningPublished by Springer Science and Business Media LLC ,1998
- Wrappers for feature subset selectionArtificial Intelligence, 1997
- Selection of relevant features and examples in machine learningArtificial Intelligence, 1997
- Feature selection for classificationIntelligent Data Analysis, 1997
- Automatic Parameter Selection by Minimizing Estimated ErrorPublished by Elsevier BV ,1995
- A Complexity MeasureIEEE Transactions on Software Engineering, 1976