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In this paper, a hybrid genetic approach is proposed to solve the problem of designing a subdatabase of the original one with the highest classification performances , the lowest number of features and the highest number of patterns. The method can simultaneously treat the double problem of editing instance patterns and selecting features as a single(More)
In this paper, a new genetic algorithm is proposed for designing 2-D FIR filters with the objective of being relevant and tractable. The key point of our approach stems in the capacity of our GA to adapt the genetic operators during the genetic life while remaining simple and easy to implement. It hybridizes the use of conventional and dedicated processes.(More)
As clustering algorithms become more and more sophisticated to cope with current needs, large data sets of increasing complexity, sampling is likely to provide an interesting alternative. The proposal is a distance-based algorithm: The idea is to iteratively include in the sample the furthest item from all the already selected ones. Density is managed(More)
This paper presents an objective and comparative study of evolutionary algorithms applied for designing two dimensional (2D) FIR filters. The design of 2-D FIR filters can be formulated as a non-linear optimization problem. We explore several stochastic methodologies capable of handling large spaces. We finally propose a new genetic algorithm where some(More)
This paper investigates a method for instance selection in the context of supervised classification adapted to large databases. Based on the scale up concept, the method reduces the time required to perform the selection procedure by enabling the application of known condensation instance techniques to only small data sets instead of the whole set. The(More)