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Title: A Taxonomy and Short Review of Ensemble Selection
Author(s): G. Tsoumakas, I. Partalas, I. Vlahavas.
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Keywords: ensemble selection.
Appeared in: ECAI, Workshop on Supervised and Unsupervised Ensemble Methods and Their Applications, Patras, Greece, 2008.
Abstract: Ensemble selection deals with the reduction of an ensemble of predictive models in order to improve its efficiency and predictive performance. The last 10 years a large number of very diverse ensemble selection methods have been proposed. In this paper we make a first approach to categorize them into a taxonomy. We also present a short review of some of these methods. We particularly focus on a category of methods that are based on greedy search of the space of all possible ensemble subsets. Such methods use different directions for searching this space and different measures for evaluating the available actions at each state. Some use the training set for subset evaluation, while others a separate validation set. This paper abstracts the key points of these methods and offers a general framework of the greedy ensemble selection algorithm, discussing its important parameters and the different options for instantiating these parameters.
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        This paper has been cited by the following:

1 Rokach,L. (2009) Collective-agreement-based pruning of ensembles, Computational Statistics and Data Analysis 53(4), 1015-1026.
2 Mohamed Farouk Abdel Hady (2010) Semi-Supervised Learning with Committees: Exploiting Unlabeled Data Using Ensemble Learning Algorithms, PhD Thesis, Faculty of Engineering and Computer Science at University of Ulm, Germany, 2010
3 Hady, M.F.A., Schwenker, F., Palm, G. (2010) When classifier selection meets information theory: A unifying view, Proceedings of the 2010 International Conference of Soft Computing and Pattern Recognition, SoCPaR 2010, art. no. 5686645, pp. 314-319.
4 Rokach, L. (2010) Ensemble-based classifiers, Artificial Intelligence Review 33(1-2), pp 1-39.
5 Toraman, C., Can, F. (2011) Ensemble pruning for text categorization based on data partitioning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7097 LNCS, pp. 352-361.
6 Smȩtek, M., Trawiński, B. (2011) Selection of heterogeneous fuzzy model ensembles using self-adaptive genetic algorithms, New Generation Computing, 29 (3), pp. 309-327.
7 Diao R., Shen, Q. (2011). Fuzzy-rough Classifier Ensemble Selection. Proceedings of the 20th International Conference on Fuzzy Systems(Fuzz-IEEE 2011), pp. 1516-1522.
8 Kokkinos,Y.;Margaritis,K.G. (2012) A distributed asynchronous and privacy preserving neural network ensemble selection approach for peer-to-peer data mining. In Proceedings of the Fifth Balkan Conference in Informatics (BCI '12). ACM, New York, NY, USA, 46-51.
9 Toraman, C., Can, F. (2012) Squeezing the ensemble pruning: Faster and more accurate categorization for news portals, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7224 LNCS, pp. 508-511.
10 Kodell, R.L., Zhang, C., Siegel, E.R., Nagarajan, R. (2012) Selective voting in convex-hull ensembles improves classification accuracy, Artificial Intelligence in Medicine, 54 (3), pp. 171-179.


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