NDLI: Selective Sampling Designs to Improve the Performance of Classification Methods

Content Provider	IEEE Xplore Digital Library
Author	Ghorbani, S. Desmarais, M.C.
Copyright Year	2013
Description	Author affiliation: Comput. & Software Eng. Dept., Polytech., Montreal, QC, Canada (Ghorbani, S.; Desmarais, M.C.)
Abstract	Selective Sampling design refers to the situation where a study has a fixed number of observations but can decide to allocate them differently among the variables during the data gathering phase, such that some variables will have a greater ratio of missing values than others. In particular, we can decide to allocate more, or less missing values to uncertain variables: those for which the relative frequency is closer to 50% (higher uncertainty), or further from 50% (lower certainty). The main objective of the study is to investigate how a Selective Sampling process helps improve the performance of classification methods. This study specifically asks: "Can Selective Sampling affect the performance of the classification methods?" We focus on the three different classification models of Naïve Bayes, Logistic Regression and Tree Augmented Naive Bayes (TAN) for binary datasets. Three different schemes of sampling are defined: 1-Uniform (random samples) as a baseline, 2-Most Uncertain (higher sampling rate of uncertain items) and 3-Least Uncertain (lower sampling rate of uncertain items). We investigate the impacts of these different schemes on the performance of the three models on 11 different datasets. The results from 100 fold cross-validation show that Selective Sampling in all of the datasets improves the prediction performance of the TAN model and, in more than half of the datasets (54.6%), brings a higher prediction performance to Naïve Bayes and Logistic Regression classifiers.
Sponsorship	IEEE Syst., Man, Cybern. Soc.
Starting Page	178
Ending Page	181
File Size	384544
Page Count	4
File Format	PDF
ISBN	9780769551449
DOI	10.1109/ICMLA.2013.187
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Selective Sampling Computational modeling Classification Planned Missing Data Design Predictive models Entropy Niobium Logistics Testing
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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