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  Ranking and selecting clustering algorithms using a meta-learning approach

de Souto, M. C., Prudencio, R. B., Soares, R. G., de Araujo, D. S., Costa, I., Ludermir, T.., et al. (2008). Ranking and selecting clustering algorithms using a meta-learning approach. In Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on (pp. 3729-3735).

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 Creators:
de Souto, Marcilio C.P, Author
Prudencio, Ricardo B.C, Author
Soares, Rodrigo G.F, Author
de Araujo, Daniel S.A, Author
Costa, Ivan1, Author              
Ludermir, Teresa .B, Author
Schliep, Alexander1, Author              
Affiliations:
1Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433547              

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 Abstract: We present a novel framework that applies a meta-learning approach to clustering algorithms. Given a dataset, our meta-learning approach provides a ranking for the candidate algorithms that could be used with that dataset. This ranking could, among other things, support non-expert users in the algorithm selection task. In order to evaluate the framework proposed, we implement a prototype that employs regression support vector machines as the meta-learner. Our case study is developed in the context of cancer gene expression micro-array datasets.

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Language(s): eng - English
 Dates: 2008-09-26
 Publication Status: Published in print
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Title: (IEEE World Congress on Computational Intelligence
Place of Event: Hong Kong
Start-/End Date: 2008-06-01 - 2008-06-08

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Title: Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 3729 - 3735 Identifier: ISSN: 1098-7576