NDLI: Document Classification in a Non-stationary Environment: A One-Class SVM Approach

Content Provider	IEEE Xplore Digital Library
Author	Ho, A.K.N. Ragot, N. Ramel, J.-Y. Eglin, V. Sidere, N.
Copyright Year	2013
Description	Author affiliation: Lab. d'Inf., Univ. Francois Rabelais Tours, Tours, France (Ho, A.K.N.; Ragot, N.; Ramel, J.-Y.; Sidere, N.) \|\| Lab. d'Inf. en Image et Syst. d'Inf., INSA de Lyon, Lyon, France (Eglin, V.)
Abstract	In this paper, we investigate a specific area of document classification in which the documents come as a flow over the time. Moreover, the exact number of classes of document to deal with is not known from the beginning and could evolve over the time. To be able to perform classification task in such area, we need specific classifiers that are able to perform incremental learning and change their modeling over the time. More specifically, we are focusing our study on SVM approaches, known to perform well, and for which incremental (i-SVM) procedures exist. Nevertheless, most of them are only able to deal with a fixed number of classes. So we designed a new incremental learning procedure based on one-class SVMs. This one is able to improve its classification accuracy over the time, with the arrival of new labeled data, without performing any complete retraining. Moreover, when instances are coming with a previously unknown label (appearance of a new class), the training procedure is able to modify the classifier model to recognize this corresponding new kind of documents. To investigate this area, waiting for collecting documents images as a flow, we did first experiments on the Optical Recognition of Handwritten Digits Data Set. These experiments show that our incremental approach is able: to perform, at each time, as well as a static one-class classifier fully retrained using all previously seen data, to model very quickly and efficiently new incoming classes.
Starting Page	616
Ending Page	620
File Size	425688
Page Count	5
File Format	PDF
ISBN	9780769549996
ISSN	15205363
DOI	10.1109/ICDAR.2013.127
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-08-25
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Accuracy Classification algorithms Data models Training data Machine learning algorithms document classification Incremental learning one-class SVM
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
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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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