NDLI: Study of SVM decision-tree optimization algorithm based on genetic algorithm

Content Provider	IEEE Xplore Digital Library
Author	Xiaoqing Yu Junwei Liu Yanfei Zhou Wanggen Wan
Copyright Year	2010
Description	Author affiliation: School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China (Xiaoqing Yu; Junwei Liu; Yanfei Zhou; Wanggen Wan)
Abstract	In this paper, we present a SVM multi-classification decision-tree optimization algorithm based on genetic algorithm (GA) in order to overcome the defect of the error accumulation which is caused by the fixed tree configuration of traditional support vector machine (SVM) multi-classification decision-tree algorithms and the random positions of their decision nodes. We adopt the “classification margin” of SVM as GA adaptive function. Then, GA is used to create optimal or suboptimal decision-tree automatically, which makes the margin between two classes maximal at every decision node. Experimental results show that the error accumulation phenomenon is weakened obviously and classification quality is advanced greatly compared with the traditional algorithms.
Starting Page	1079
Ending Page	1083
File Size	259847
Page Count	5
File Format	PDF
ISBN	9781424458561
e-ISBN	9781424458585
DOI	10.1109/ICALIP.2010.5685104
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Encoding Classification algorithms Biological cells 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	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

Decision Tree Support Vector Machine based on Genetic Algorithm for fault diagnosis

A parameter selection of support vector machine with genetic algorithm for citrus quality classification

Study on Support Vector Machine Based Decision Tree and Application

Research on attribute interval optimization method for segmentation based SVM and the Decision Tree Learning

An improved GA-SVM algorithm

Multi-modal Tree-Based SVM Classification

Performance enhancement of SVM ensembles using genetic algorithms in bankruptcy prediction

Semi-supervised multitemporal classification with support vector machines and genetic algorithms

Hybrid Method for Fast SVM Training in Applications Involving Large Volumes of Data

Study of SVM decision-tree optimization algorithm based on genetic algorithm

Similar Documents

Decision Tree Support Vector Machine based on Genetic Algorithm for fault diagnosis

A parameter selection of support vector machine with genetic algorithm for citrus quality classification

Study on Support Vector Machine Based Decision Tree and Application

Research on attribute interval optimization method for segmentation based SVM and the Decision Tree Learning

An improved GA-SVM algorithm

Multi-modal Tree-Based SVM Classification

Performance enhancement of SVM ensembles using genetic algorithms in bankruptcy prediction

Semi-supervised multitemporal classification with support vector machines and genetic algorithms

Hybrid Method for Fast SVM Training in Applications Involving Large Volumes of Data

Study of SVM decision-tree optimization algorithm based on genetic algorithm