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Ranking and selecting clustering algorithms using a meta-learning approach

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50127

Costa,  Ivan
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50523

Schliep,  Alexander
Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Zitation

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).


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-7F00-1
Zusammenfassung
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.