Comparison between two coevolutionary feature weighting algorithms in clustering

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 charac...

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Bibliographic Details
Published inPattern recognition Vol. 41; no. 3; pp. 983 - 994
Main Authors Gançarski, P., Blansché, A., Wania, A.
Format Journal Article
LanguageEnglish
Published Oxford Elsevier Ltd 01.03.2008
Elsevier Science
Subjects
Applied sciences
Complex data
Cooperative coevolution
Exact sciences and technology
Feature weighting
Image processing
Information, signal and communications theory
Modular clustering
Signal and communications theory
Signal processing
Signal representation. Spectral analysis
Signal, noise
Telecommunications and information theory
Complex data
Modular clustering
Feature weighting
Cooperative coevolution
Automatic classification
Partition method
Image processing
Inheritance
Coevolution
Signal classification
Optimization
Remote sensing
Hyperspectral imaging sensor
Weighting
Genetic algorithm
Quality control
Cost function
Distance measurement
Image classification
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Summary: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.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2007.07.008