Article

C³E: a framework for combining ensembles of classifiers and clusterers

Authors:

A. Acharya,

E. R. Hruschka,

J. Ghosh,

S. AcharyyaAuthors Info & Claims

MCS'11: Proceedings of the 10th international conference on Multiple classifier systems

Pages 269 - 278

Published: 15 June 2011 Publication History

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Abstract

The combination of multiple classifiers to generate a single classifier has been shown to be very useful in practice. Similarly, several efforts have shown that cluster ensembles can improve the quality of results as compared to a single clustering solution. These observations suggest that ensembles containing both classifiers and clusterers are potentially useful as well. Specifically, clusterers provide supplementary constraints that can improve the generalization capability of the resulting classifier. This paper introduces a new algorithm named C³E that combines ensembles of classifiers and clusterers. Our experimental evaluation of C³E shows that it provides good classification accuracies in eleven tasks derived from three real-world applications. In addition, C³E produces better results than the recently introduced Bipartite Graph-based Consensus Maximization (BGCM) Algorithm, which combines multiple supervised and unsupervised models and is the algorithm most closely related to C³E.

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Cited By

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Vannucci MColla VCateni S(2017)Learners Reliability Estimated Through Neural Networks Applied to Build a Novel Hybrid Ensemble MethodNeural Processing Letters10.1007/s11063-017-9586-646:3(791-809)Online publication date: 1-Dec-2017
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(2016)Using unsupervised information to improve semi-supervised tweet sentiment classificationInformation Sciences: an International Journal10.1016/j.ins.2016.02.002355:C(348-365)Online publication date: 10-Aug-2016
https://dl.acm.org/doi/10.1016/j.ins.2016.02.002
Acharya AHruschka EGhosh JAcharyya S(2014)An Optimization Framework for Combining Ensembles of Classifiers and Clusterers with Applications to Nontransductive Semisupervised Learning and Transfer LearningACM Transactions on Knowledge Discovery from Data10.1145/26014359:1(1-35)Online publication date: 25-Aug-2014
https://dl.acm.org/doi/10.1145/2601435
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Published In

MCS'11: Proceedings of the 10th international conference on Multiple classifier systems

June 2011

371 pages

ISBN:9783642215568

Editors:
Carlo Sansone
Università di Napoli Federico II, Dipartimento di Informatica e Sistemistica, Napoli, Italy
,
Josef Kittler
University of Surrey, Centre for Vision, Speech and Signal Processing, Guildford, Surrey, UK
,
Fabio Roli
University of Cagliari, Department of Electrical and Electronic Engineering, Cagliari, Italy

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 15 June 2011

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Vannucci MColla VCateni S(2017)Learners Reliability Estimated Through Neural Networks Applied to Build a Novel Hybrid Ensemble MethodNeural Processing Letters10.1007/s11063-017-9586-646:3(791-809)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1007/s11063-017-9586-6
(2016)Using unsupervised information to improve semi-supervised tweet sentiment classificationInformation Sciences: an International Journal10.1016/j.ins.2016.02.002355:C(348-365)Online publication date: 10-Aug-2016
https://dl.acm.org/doi/10.1016/j.ins.2016.02.002
Acharya AHruschka EGhosh JAcharyya S(2014)An Optimization Framework for Combining Ensembles of Classifiers and Clusterers with Applications to Nontransductive Semisupervised Learning and Transfer LearningACM Transactions on Knowledge Discovery from Data10.1145/26014359:1(1-35)Online publication date: 25-Aug-2014
https://dl.acm.org/doi/10.1145/2601435
Jin YZeng WZhuo HLi L(2013)Ensemble of Unsupervised and Supervised Models with Different Label SpacesPart II of the Proceedings of the 9th International Conference on Advanced Data Mining and Applications - Volume 834710.1007/978-3-642-53917-6_42(466-477)Online publication date: 14-Dec-2013
https://dl.acm.org/doi/10.1007/978-3-642-53917-6_42

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Classification by cluster analysis: a new meta-learning based approach

Cluster-Oriented Ensemble Classifier: Impact of Multicluster Characterization on Ensemble Classifier Learning

Using boosting to prune bagging ensembles

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