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Ranking and selecti...
Ranking and selecting clustering algorithms using a meta-learning approach
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De Souto, Marcilio C P (author)
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Prudêncio, Ricardo B C (author)
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Soares, Rodrigo G F (author)
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De Araujo, Daniel S A (author)
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Costa, Ivan G. (author)
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Ludermir, Teresa B. (author)
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- Schliep, Alexander, 1967 (author)
- Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik, datavetenskap (GU),Department of Computer Science and Engineering, Computing Science (GU)
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(creator_code:org_t)
- 2008
- 2008
- English.
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In: Proceedings of the International Joint Conference on Neural Networks.
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https://doi.org/10.1...
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Abstract
Subject headings
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- 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 microarray datasets. © 2008 IEEE.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Bioinformatik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Bioinformatics (hsv//eng)
Publication and Content Type
- ref (subject category)
- kon (subject category)
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