LPIS Home Page
Google Search

Title: Focused Ensemble Selection: A Diversity-Based Method for Greedy Ensemble Selection
Author(s): I. Partalas, G. Tsoumakas, I. Vlahavas.
Availability: Click here to download the PDF (Acrobat Reader) file (5 pages).
Keywords: ensemble selection.
Appeared in: 18th European Conference on Artificial Intelligence, IOS Press, pp. 117-121, 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. A number of ensemble selection methods that are based on greedy search of the space of all possible ensemble subsets have recently been proposed. This paper contributes a novel method, based on a new diversity measure that takes into account the strength of the decision of the current ensemble. Experimental comparison of the proposed method, dubbed Focused Ensemble Selection (FES), against state-of-the-art greedy ensemble selection methods shows that it leads to small ensembles with high predictive performance.
See also :

        This paper has been cited by the following:

1 Lu, Z., Wu, X., Zhu, X., Bongard, J. (2010) Ensemble Pruning via Individual Contribution Ordering, The 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Washington DC, 2010.
2 Khreich, W. (2011) Towards Adaptive Anomaly Detection Systems using Boolean Combination of Hidden Markov Models, PhD Thesis, École De Technologie Superieure, Universite Du Quebec, Canada.
3 Guo, H., Zhi, W., Han, X., Fan, M. (2011) A new metric for greedy ensemble pruning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7003 LNAI (PART 2), pp. 631-639.
4 Zhang, G., Yin, J., Zhang, S., Cheng, L. (2011) Regularization based ordering for ensemble pruning, Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, 2, art. no. 6019643, pp. 1325-1329.
5 Guo, H., Fan, M. (2011) Ensemble Pruning via Base-Classifier Replacement, Proc. 12th International Conference, WAIM 2011, Wuhan, China, September 14-16, 2011, pp. 505-516
6 Mao, S., Jiao, L.C., Xiong, L., Gou, A. (2011), Greedy optimization classifiers ensemble based on diversity, Pattern Recognition 44(6), Pages 1245-1261.
7 Fu,B.;Wang,Z.;Pan,R.;Xu,G.;Dolog,P. (2012) An Integrated Pruning Criterion for Ensemble Learning Based on Classification Accuracy and Diversity, Proceedings 7th International Conference on Knowledge Management in Organizations: Service and Cloud Computing, pp. 47-58.
8 Li,N.;Yu,Y.;Zhou,Z.-H. (2012) Diversity Regularized Ensemble Pruning, Proc. ECML PKDD 2012, pp. 330-345.
9 Re,M.;Valentini,G. (2012) Ensemble methods: a review, In: Advances in Machine Learning and Data Mining for Astronomy, Chapman and Hall Data Mining and Knowledge Discovery Series, Chap. 26, pp. 563-594, 2012.
10 Zhi, W., Guo, H., Fan, M. (2012) Energy-based metric for ensemble selection, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7235 LNCS, pp. 306-317.
11 Khreich, W., Granger, E., Miri, A., Sabourin, R. (2012) Adaptive ROC-based ensembles of HMMs applied to anomaly detection, Pattern Recognition, 45 (1), pp. 208-230.