NDLI: An ensemble of classifiers approach for the missing feature problem

Content Provider	IEEE Xplore Digital Library
Author	Krause, S. Polikar, R.
Copyright Year	2003
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Rowan Univ., Glassboro, NJ, USA (Krause, S.; Polikar, R.)
Abstract	A new learning algorithm is introduced that can accommodate data with missing features. The algorithm uses an ensemble of classifiers approach. The classifiers in the ensemble are trained with random subsets of the total number of available features. The approach takes advantage of the basic assumption that an unknown subset of the features is in fact adequate for the classification, or in other words, that are redundant, and possibly irrelevant features in the data. This assumption is in general true for most practical applications. We empirically show that if a certain number of networks produce a particular classification performance using all of the features, then the same classification performance can be reached even if some features are missing, as long as the same number of usable networks can be generated with the missing features. The proposed approach has its roots in the incremental learning algorithm, Learn/sup ++/ which seeks to learn new information that is provided by additional datasets that may later become available, even when such data introduce new classes. We have modified the Learn/sup ++/ algorithm for addressing the missing feature problem. The proposed algorithm showed surprisingly remarkable performance on three real-world applications, with up to 10% of the features missing in the validation / field data.
Sponsorship	Int. Neural Network Soc. IEEE Neural Networks Soc
Starting Page	553
Ending Page	558
File Size	491146
Page Count	6
File Format	PDF
ISBN	0780378989
ISSN	10987576
DOI	10.1109/IJCNN.2003.1223406
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2003-07-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training data Diversity reception Classification algorithms Neural networks Fixtures Dairy products Bayesian methods Data mining Voting
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	Mr. Siddhartha Mukherjee siddhartha@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	ndl-support@iitkgp.ac.in
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7	National Digital Libarray 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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