NDLI: A comparative study of feature-ranking and feature-subset selection techniques for improved fault prediction

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A comparative study of feature-ranking and feature-subset selection techniques for improved fault prediction

Content Provider	ACM Digital Library
Author	Rathore, Santosh Singh Gupta, Atul
Abstract	The quality of a fault prediction model depends on the software metrics that are used to build the prediction model. Feature selection represents a process of selecting a subset of relevant features that may lead to build improved prediction models. Feature selection techniques can be broadly categorized into two subcategories: feature-ranking and feature-subset selection. In this paper, we present a comparative investigation of seven feature-ranking techniques and eight feature-subset selection techniques for improved fault prediction. The performance of these feature selection techniques is evaluated using two popular machine-learning classifiers: Naive Bayes and Random Forest, over fourteen software project's fault-datasets obtained from the PROMISE data repository. The performances were measured using F-measure and AUC values. Our results demonstrated that feature-ranking techniques produced better results compared to feature-subset selection techniques. Among, the feature-ranking techniques used in the study, InfoGain and PCA techniques provided the best performance over all the datasets, while for feature-subset selection techniques ClassifierSubsetEval and Logistic Regression produced better results against their peers.
Starting Page	1
Ending Page	10
Page Count	10
File Format	PDF
ISBN	9781450327763
DOI	10.1145/2590748.2590755
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-02-19
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Feature-ranking Wrappers Feature selection Filters Software metrics Fault prediction
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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