Abstract
The problem addressed in this paper concerns data reduction through instance selection. The paper proposes an approach based on instance selection from clusters. The process of selection and learning is executed by a team of agents. The approach aims at obtaining a compact representation of the dataset, where the upper bound on the size of data is determined by the user. The basic assumption is that the instance selection is carried out after the training data have been grouped into clusters. The cluster initialization and integration strategies are proposed and experimentally evaluated.
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Czarnowski, I., Jędrzejowicz, P. (2010). Cluster Integration for the Cluster-Based Instance Selection. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2010. Lecture Notes in Computer Science(), vol 6421. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16693-8_37
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DOI: https://doi.org/10.1007/978-3-642-16693-8_37
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