NDLI: Document Classification Based on Support Vector Machine Using a Concept Vector Model

Content Provider	IEEE Xplore Digital Library
Author	Shuang Deng Hong Peng
Copyright Year	2006
Description	Author affiliation: Sch. of Math. & Comput. Sci., Xihua Univ., Sichuan (Shuang Deng)
Abstract	This paper proposes a new method for document categorization, based on support vector machine (SVM) using a concept vector model (CVM). The traditional document classification usually ignores the semantic relations among the keywords or documents. To effectively solve the semantic problem, the domain ontology is used to capture the semantic information among different terms or keywords in the documents. Using the concept vector model, domain-related semantic information more exactly from documents can be extracted. In the model, concept vector is extracted from a document by the matching method. According to concept features of the documents, documents are classified into a suitable category by SVM. The experimental results show that our CVM method yields higher accuracy compared to the traditional term-based vector space model (VSM) methods
Sponsorship	IEEE Comput. Soc. WIC ACM
Starting Page	473
Ending Page	476
File Size	116525
Page Count	4
File Format	PDF
ISBN	0769527477
DOI	10.1109/WI.2006.65
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-12-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Computer science Support vector machine classification Ontologies Probability Mathematics Data mining Mathematical model Decision trees Classification tree analysis
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
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
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

Concept Classification Using a Hybrid Data Mining Model

A Classification Method Based on Non-linear SVM Decision Tree

Efficient classification and analysis of ischemic heart disease using proximal support vector machines based decision trees

A Parallel Multi-Class Classification Support Vector Machine Based on Sequential Minimal Optimization

A New Multi-class Classification Based on Non-linear SVM and Decision Tree

A Hybrid Credit Scoring Model Based on Genetic Programming and Support Vector Machines

Rule extraction using Support Vector Machine based hybrid classifier

Region Classification with Decision Trees

An Improved Binary Tree SVM Classification Algorithm Based on Bayesian

Document Classification Based on Support Vector Machine Using a Concept Vector Model

Similar Documents

Concept Classification Using a Hybrid Data Mining Model

A Classification Method Based on Non-linear SVM Decision Tree

Efficient classification and analysis of ischemic heart disease using proximal support vector machines based decision trees

A Parallel Multi-Class Classification Support Vector Machine Based on Sequential Minimal Optimization

A New Multi-class Classification Based on Non-linear SVM and Decision Tree

A Hybrid Credit Scoring Model Based on Genetic Programming and Support Vector Machines

Rule extraction using Support Vector Machine based hybrid classifier

Region Classification with Decision Trees

An Improved Binary Tree SVM Classification Algorithm Based on Bayesian

Document Classification Based on Support Vector Machine Using a Concept Vector Model