NDLI: A comparative study of filter-based feature ranking techniques

Content Provider	IEEE Xplore Digital Library
Author	Huanjing Wang Khoshgoftaar, T.M. Kehan Gao
Copyright Year	2010
Description	Author affiliation: Eastern Connecticut State University, Willimantic, 06226, USA (Kehan Gao) \|\| Western Kentucky University, Bowling Green, 42101, USA (Huanjing Wang) \|\| Florida Atlantic University, Boca Raton, 33431, USA (Khoshgoftaar, T.M.)
Abstract	One factor that affects the success of machine learning is the presence of irrelevant or redundant information in the training data set. Filter-based feature ranking techniques (rankers) rank the features according to their relevance to the target attribute and we choose the most relevant features to build classification models subsequently. In order to evaluate the effectiveness of different feature ranking techniques, a commonly used method is to assess the classification performance of models built with the respective selected feature subsets in terms of a given performance metric (e.g., classification accuracy or misclassification rate). Since a given performance metric usually can capture only one specific aspect of the classification performance, it may be unable to evaluate the classification performance from different perspectives. Also, there is no general consensus among researchers and practitioners regarding which performance metrics should be used for evaluating classification performance. In this study, we investigated six filter-based feature ranking techniques and built classification models using five different classifiers. The models were evaluated using eight different performance metrics. All experiments were conducted on four imbalanced data sets from a telecommunications software system. The experimental results demonstrate that the choice of a performance metric may significantly influence the classification evaluation conclusion. For example, one ranker may outperform another when using a given performance metric, but for a different performance metric the results may be reversed. In this study, we have found five distinct patterns when utilizing eight performance metrics to order six feature selection techniques.
Starting Page	43
Ending Page	48
File Size	362691
Page Count	6
File Format	PDF
ISBN	9781424480975
e-ISBN	9781424480999
DOI	10.1109/IRI.2010.5558966
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Measurement Radio frequency Support vector machines Niobium Software Analysis of variance Software algorithms
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
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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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