NDLI: A semilabeled-sample-driven bagging technique for ill-posed classification problems

Content Provider	IEEE Xplore Digital Library
Author	Mingmin Chi Bruzzone, L.
Copyright Year	2004
Abstract	In this letter, a semilabeled-sample-driven bootstrap aggregating (bagging) technique based on a co-inference (inductive and transductive) framework is proposed for addressing ill-posed classification problems. The novelties of the proposed technique lie in: 1) the definition of a general classification strategy for ill-posed problems by the joint use of training and semilabeled samples (i.e., original unlabeled samples labeled by the classification process); and 2) the design of an effective bagging method (driven by semilabeled samples) for a proper exploitation of different classifiers based on bootstrapped hybrid training sets. Although the proposed technique is general and can be applied to any classification algorithm, in this letter multilayer perceptron neural networks (MLPs) are used to develop the basic classifier of the proposed architecture. In this context, a novel cost function for the training of MLPs is defined, which properly considers the contribution of semilabeled samples in the learning of each member of the ensemble. The experimental results, which are obtained on different ill-posed classification problems, confirm the effectiveness of the proposed technique.
Sponsorship	IEEE Geoscience and Remote Sensing Society
Starting Page	69
Ending Page	73
Page Count	5
File Size	202285
File Format	PDF
ISSN	1545598X
Volume Number	2
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bagging Remote sensing Multilayer perceptrons Neural networks Multi-layer neural network Cost function Classification algorithms Hyperspectral sensors Hyperspectral imaging Communications technology supervised classification ill-posed classification problems multiple classifier systems remote sensing images semilabeled samples
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Geotechnical Engineering and Engineering Geology

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 fuzzy-input fuzzy-output SVM technique for classification of hyperspectral remote sensing images

Ensemble Classification Algorithm for Hyperspectral Remote Sensing Data

Sample labeling and classification method of hyperspectral remote sensing images based on texture features and semi-supervised learning

Feature Evolution for Classification of Remotely Sensed Data

Automated generation of semi-labeled training samples for nonlinear neural network-based abundance estimation in hyperspectral data

An efficient classifier design for remote sensing hyperspectral imagery

BandClust: An Unsupervised Band Reduction Method for Hyperspectral Remote Sensing

Feature reduction of hyperspectral data using Autoassociative neural networks algorithms

A fuzzy spatio-temporal contextual classifier for remote sensing images

A semilabeled-sample-driven bagging technique for ill-posed classification problems

Similar Documents

A fuzzy-input fuzzy-output SVM technique for classification of hyperspectral remote sensing images

Ensemble Classification Algorithm for Hyperspectral Remote Sensing Data

Sample labeling and classification method of hyperspectral remote sensing images based on texture features and semi-supervised learning

Feature Evolution for Classification of Remotely Sensed Data

Automated generation of semi-labeled training samples for nonlinear neural network-based abundance estimation in hyperspectral data

An efficient classifier design for remote sensing hyperspectral imagery

BandClust: An Unsupervised Band Reduction Method for Hyperspectral Remote Sensing

Feature reduction of hyperspectral data using Autoassociative neural networks algorithms

A fuzzy spatio-temporal contextual classifier for remote sensing images

A semilabeled-sample-driven bagging technique for ill-posed classification problems