NDLI: Build decision tree on support vector machine

Content Provider	IEEE Xplore Digital Library
Author	Dexian Zhang Xiao-Bo Jin
Copyright Year	2011
Description	Author affiliation: School of Information Science and Engineering, Henan University of Technology, China (Dexian Zhang; Xiao-Bo Jin)
Abstract	C4.5 is a popular classification method which can give the explainable and intuitional classification rules. But it is prone to overfitting due to the data noise or the distribution of the instances. In this paper, we proposed a new decision tree method with the support vector machine (SVM-DTR), which make the surface of the decision tree to discriminate the instances from the different categories as far as possible. SVMis used to measure the importance of the attribute on the fact that the cosine of the angle between the attribute axis and the normal of the decision surface can quantize its significance. Similar as the C4.5, each time we choose the most important attribute as the root of the sub-tree. We analyze the influence of the kernel width to the magnitude of the gradient and obtain the empirical settings about the kernel width from the experiments. The comparisons between the SVM-DTR and the C4.5 on 5 datasets from UCI machine learning repository show that SVM-DTR achieve the better performance than C4.5.
Starting Page	997
Ending Page	1001
File Size	147549
Page Count	5
File Format	PDF
ISBN	9781612841809
e-ISBN	9781612841816
DOI	10.1109/FSKD.2011.6019745
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Spirals Machine learning Educational institutions Decision trees Kernel
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

A new ensemble learning with support vector machines

An Improved Hyper-sphere Support Vector Machine

Weighted multi-class support vector machine based on binary tree

Hierarchical support vector machines

The research of decision support vector machine in web information classification

Support vector machine based decision tree for very high resolution multispectral forest mapping

Region Classification with Decision Trees

An experimental evaluation of pairwise adaptive support vector machines

Multi-class classification using support vector machines in decision tree architecture

Build decision tree on support vector machine

Similar Documents

A new ensemble learning with support vector machines

An Improved Hyper-sphere Support Vector Machine

Weighted multi-class support vector machine based on binary tree

Hierarchical support vector machines

The research of decision support vector machine in web information classification

Support vector machine based decision tree for very high resolution multispectral forest mapping

Region Classification with Decision Trees

An experimental evaluation of pairwise adaptive support vector machines

Multi-class classification using support vector machines in decision tree architecture

Build decision tree on support vector machine