NDLI: Clustering-based binary-class classification for imbalanced data sets

Content Provider	IEEE Xplore Digital Library
Author	Chao Chen Mei-Ling Shyu
Copyright Year	2011
Description	Author affiliation: Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33124, USA (Chao Chen; Mei-Ling Shyu)
Abstract	In this paper, we propose a new clustering-based binary-class classification framework that integrates the clustering technique into a binary-class classification approach to handle the imbalanced data sets. A binary-class classifier is designed to classify a set of data instances into two classes; while the clustering technique partitions the data instances into groups according to their similarity to each other. After applying a clustering algorithm, the data instances within the same group usually have a higher similarity, and the differences among the data instances between different groups should be larger. In our proposed framework, all negative data instances are first clustered into a set of negative groups. Next, the negative data instances in each negative group are combined with all positive data instances to construct a balanced binary-class data set. Finally, subspace models trained on these balanced binary-class data sets are integrated with the subspace model trained on the original imbalanced data set to form the proposed classification model. Experimental results demonstrate that our proposed classification framework performs better than the comparative classification approaches as well as the subspace modeling method trained on the original data set alone.
Starting Page	384
Ending Page	389
File Size	156485
Page Count	6
File Format	PDF
ISBN	9781457709647
e-ISBN	9781457709661
e-ISBN	9781457709654
DOI	10.1109/IRI.2011.6009578
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data models Testing Optimized production technology Training Training data Support vector machines Videos Clustering Binary classification Subspace Modeling Imbalanced data sets
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

Centroid based Binary Tree Structured SVM for multi classification

New Oversampling Approaches Based on Polynomial Fitting for Imbalanced Data Sets

CLUS: A new hybrid sampling classification for imbalanced data

Active learning support vector machines to classify imbalanced reservoir simulation data

A one-class classification framework using SVDD: Application to an imbalanced geological dataset

One-Class versus Binary Classification: Which and When?

Weighted one-class classification for different types of minority class examples in imbalanced data

Multi-Class Support Vector Machines for Large Data Sets via Minimum Enclosing Ball Clustering

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

Clustering-based binary-class classification for imbalanced data sets

Similar Documents

Centroid based Binary Tree Structured SVM for multi classification

New Oversampling Approaches Based on Polynomial Fitting for Imbalanced Data Sets

CLUS: A new hybrid sampling classification for imbalanced data

Active learning support vector machines to classify imbalanced reservoir simulation data

A one-class classification framework using SVDD: Application to an imbalanced geological dataset

One-Class versus Binary Classification: Which and When?

Weighted one-class classification for different types of minority class examples in imbalanced data

Multi-Class Support Vector Machines for Large Data Sets via Minimum Enclosing Ball Clustering

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

Clustering-based binary-class classification for imbalanced data sets