Comparison between two coevolutionary feature weighting algorithms in clustering |
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Authors: | P Gançarski A Blansché A Wania |
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Affiliation: | 1. Department of Statistical Science, University College London, London WC1E 6BT, UK;2. Department of Computer Science, Graduate School of Systems and Information Engineering, University of Tsukuba, Ibaraki 305-8573, Japan;1. Stat-math Unit, Indian Statistical Institute, Kolkata 700108, India;2. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata 700108, India\n |
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Abstract: | Feature weighting is an aspect of increasing importance in clustering because data are becoming more and more complex nowadays. In this paper, we propose two new feature weighting methods based on coevolutive algorithms. The first one is inspired by the Lamarck theory (inheritance of acquired characteristics) and uses the distance-based cost function defined in the LKM algorithm as fitness function. The second method uses a fitness function based on a new partitioning quality measure. It does not need a distance-based measure. We compare classical hill-climbing optimization with these new genetic algorithms on three data sets from UCI. Results show that the proposed methods are better than the hill-climbing based algorithms. We also present a process of hyperspectral remotely sensed image classification. The experiments, corroborated by geographers, highlight the benefits of using coevolutionary feature weighting methods to improve knowledge discovery process. |
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