NDLI: Effective pruning of neural network classifier ensembles

Content Provider	IEEE Xplore Digital Library
Author	Lazarevic, A. Obradovic, Z.
Copyright Year	2001
Description	Author affiliation: Center for Inf. Sci. & Technol., Temple Univ., Philadelphia, PA, USA (Lazarevic, A.)
Abstract	Neural network ensemble techniques have been shown to be very accurate classification techniques. However, in some real-life applications a number of classifiers required to achieve a reasonable accuracy is enormously large and hence very space consuming. The paper proposes several methods for pruning neural network ensembles. The clustering based approach applies k-means clustering to entire set of classifiers in order to identify the groups of similar classifiers and then eliminates redundant classifiers inside each cluster. Another proposed approach contains the sequence of the depth-first building the tree of classifiers according to their diversity followed by the process of tree pruning. The proposed methods applied to several data sets have shown that by selecting an optimal subset of neural network classifiers, it is possible to obtain significantly smaller ensemble of classifiers while achieving the same or even slightly better generalizability as when using the entire ensemble.
Sponsorship	Int. Neural Network Soc.
Starting Page	796
Ending Page	801
File Size	605681
Page Count	6
File Format	PDF
ISBN	0780370449
ISSN	10987576
DOI	10.1109/IJCNN.2001.939461
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-07-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Classification tree analysis Boosting Information science Machine learning Bagging Accuracy Design methodology Decision trees Diversity reception
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