Now showing items 1-5 of 5
Supervised learning by means of accuracy-aware evolutionary algorithms [Article]
This paper describes a new approach, HIerarchical DEcision Rules (HIDER), for learning generalizable rules in continuous and discrete domains based on evolutionary algorithms. The main contributions of our approach are ...
Obtaining optimal quality measures for quantitative association rules [Article]
There exist several works in the literature in which fitness functions based on a combination of weighted measures for the discovery of association rules have been proposed. Nevertheless, some differences in the measures ...
Evolutionary Generalized Radial Basis Function neural networks for improving prediction accuracy in gene classification using feature selection [Article]
Radial Basis Function Neural Networks (RBFNNs) have been successfully employed in several function approximation and pattern recognition problems. The use of different RBFs in RBFNN has been reported in the literature ...
Selecting the best measures to discover quantitative association rules [Article]
The majority of the existing techniques to mine association rules typically use the support and the confidence to evaluate the quality of the rules obtained. However, these two measures may not be sufficient to properly ...
Feature selection to enhance a two-stage evolutionary algorithm in product unit neural networks for complex classification problems [Article]
This paper combines feature selection methods with a two-stage evolutionary classifier based on product unit neural networks. The enhanced methodology has been tried out with four filters using 18 data sets that report ...