Evolutionary Generation of Prototypes for a Learning Vector Quantization Classifier

Cordella, Luigi P.; Stefano, Claudio De; Fontanella, Francesco; Marcelli, Angelo

doi:10.1007/11732242_35

Cited by 2 publications

(1 citation statement)

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“…In this context, systems based on Evolutionary Algorithms (EAs) seem to offer an effective methodology, as they are based on a powerful tool for finding solutions in complex high dimensional search spaces, where there is no a priori information about the samples distribution [6][7][8]. They typically work on a population of individuals each one representing a possible solution of the problem to be solved, which can be encoded in many different way.…”

Section: Introductionmentioning

confidence: 99%

A feature selection algorithm for class discrimination improvement

Stefano

Fontanella

Marrocco

et al. 2007

2007 IEEE International Geoscience and Remote Sensing Symposium

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Abstract-We propose a new feature selection algorithm for remote sensing image classification. Our approach has been especially devised for applications in which there is a large number of different features that can be potentially selected, implying that the search space is complex and high-dimensional. In this framework, our proposal is that of reformulating the feature selection problem as the search for the optimal subspace in which the different classes are more effectively discriminated. The search has been performed by using a genetic algorithm in which each individual encode the choice of a subspace, and its fitness is a measure of the class seperability in that subspace. The experimental results, performed on two databases, confirmed the effectiveness of the approach.

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Section: Introductionmentioning

confidence: 99%