Abstract
Feature weighting is a more and more important step in clustering because data become more and more complex.
An embedded local feature weighting method has been proposed in [1].
In this paper, we present a new method based on the same cost function, but performed through a genetic algorithm. The learning process can be performed through an evolutionary approach or through a cooperavive coevolutionary approach. Moreover, the genetic algorithm can be combined with the original Weighting K-means algorithm in a Lamarckian learning paradigm.
We compare hill-climbing optimization versus genetic algorithms, evolutionary versus coevolutionary approaches, and Darwinian versus Lamarckian learning on different datasets.
The results seem to show that, on the datasets where the original algorithm is efficient, the proposed methods are even better.
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Blansché, A., Gançarski, P., Korczak, J.J. (2005). Genetic Algorithms for Feature Weighting: Evolution vs. Coevolution and Darwin vs. Lamarck. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_69
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DOI: https://doi.org/10.1007/11579427_69
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