NDLI: An Optimization Framework for Combining Ensembles of Classifiers and Clusterers with Applications to Nontransductive Semisupervised Learning and Transfer Learning

Please wait, while we are loading the content...

An Optimization Framework for Combining Ensembles of Classifiers and Clusterers with Applications to Nontransductive Semisupervised Learning and Transfer Learning

A Framework for Exploiting Local Information to Enhance Density Estimation of Data Streams

Bayesian Variable Selection in Linear Regression in One Pass for Large Datasets

Structured Sparse Boosting for Graph Classification

GBAGC: A General Bayesian Framework for Attributed Graph Clustering

Uncovering Hierarchical and Overlapping Communities with a Local-First Approach

A Generic Multilabel Learning-Based Classification Algorithm Recommendation Method

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 8

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 7

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 6

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 5

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 4

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 3

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 2

ACM Transactions on Knowledge Discovery from Data (TKDD) : Volume 1

An Optimization Framework for Combining Ensembles of Classifiers and Clusterers with Applications to Nontransductive Semisupervised Learning and Transfer Learning

Content Provider	ACM Digital Library
Author	Acharya, Ayan Ghosh, Joydeep Hruschka, Eduardo R. Acharyya, Sreangsu
Copyright Year	2014
Abstract	Unsupervised models can provide supplementary soft constraints to help classify new “target” data because similar instances in the target set are more likely to share the same class label. Such models can also help detect possible differences between training and target distributions, which is useful in applications where concept drift may take place, as in transfer learning settings. This article describes a general optimization framework that takes as input class membership estimates from existing classifiers learned on previously encountered “source” (or training) data, as well as a similarity matrix from a cluster ensemble operating solely on the target (or test) data to be classified, and yields a consensus labeling of the target data. More precisely, the application settings considered are nontransductive semisupervised and transfer learning scenarios where the training data are used only to build an ensemble of classifiers and are subsequently discarded before classifying the target data. The framework admits a wide range of loss functions and classification/clustering methods. It exploits properties of Bregman divergences in conjunction with Legendre duality to yield a principled and scalable approach. A variety of experiments show that the proposed framework can yield results substantially superior to those provided by naïvely applying classifiers learned on the original task to the target data. In addition, we show that the proposed approach, even not being conceptually transductive, can provide better results compared to some popular transductive learning techniques.
Starting Page	1
Ending Page	35
Page Count	35
File Format	PDF
ISSN	15564681
e-ISSN	1556472X
DOI	10.1145/2601435
Volume Number	9
Issue Number	1
Journal	ACM Transactions on Knowledge Discovery from Data (TKDD)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-08-25
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Classification, clustering Ensembles Semisupervised learning Transductive learning Transfer learning
Content Type	Text
Resource Type	Article
Subject	Computer Science

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

Clustering-Based Ensembles as an Alternative to Stacking

An Optimization Framework for Semi-Supervised and Transfer Learning using Multiple Classifiers and Clusterers

An Optimization Framework for Semi-Supervised and Transfer Learning using Multiple Classifiers and Clusterers (2012)

Multiclass Semisupervised Learning Based Upon Kernel Spectral Clustering

Semisupervised Classification for Hyperspectral Imagery With Transductive Multiple-Kernel Learning

Framework for Active Clustering With Ensembles

A Low-Rank Approximation-Based Transductive Support Tensor Machine for Semisupervised Classification

Semisupervised Classification With Cluster Regularization

Transductive Multilabel Learning via Label Set Propagation

An Optimization Framework for Combining Ensembles of Classifiers and Clusterers with Applications to Nontransductive Semisupervised Learning and Transfer Learning

Similar Documents

Clustering-Based Ensembles as an Alternative to Stacking

An Optimization Framework for Semi-Supervised and Transfer Learning using Multiple Classifiers and Clusterers

An Optimization Framework for Semi-Supervised and Transfer Learning using Multiple Classifiers and Clusterers (2012)

Multiclass Semisupervised Learning Based Upon Kernel Spectral Clustering

Semisupervised Classification for Hyperspectral Imagery With Transductive Multiple-Kernel Learning

Framework for Active Clustering With Ensembles

A Low-Rank Approximation-Based Transductive Support Tensor Machine for Semisupervised Classification

Semisupervised Classification With Cluster Regularization

Transductive Multilabel Learning via Label Set Propagation

An Optimization Framework for Combining Ensembles of Classifiers and Clusterers with Applications to Nontransductive Semisupervised Learning and Transfer Learning