Sensitivity Analysis for Selective Learning by Feedforward Neural Networks
Fundamenta Informaticae
An algorithm for sample and data dimensionality reduction using fast simulated annealing
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
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The aim of this paper is present a novel method of data sample reduction for classification of interval information. Its concept is based on the sensitivity analysis, inspired by artificial neural networks, while the goal is to increase the number of proper classifications and primarily, calculation speed. The presented procedure was tested for the data samples representing classes obtained by random generator, real data from repository, with clustering also being used.