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This paper presents an application of neural network interleaved training algorithm proposed in in the domain of chess. In order to use the referenced learning method a structure of metric space is introduced in the space of chess moves. Neural network is used as a classifier of a distance from a given move to the optimal one, leading to significant limitation of the set of moves potentially worth...
Ensemble methods like bagging combine the decisions of multiple classifiers in order to obtain more accuracy than a single classifier. This paper studies the use of bagging for a region oriented symbolic classifier. Experiments with two artificial data sets, generated according to bi-variate normal distributions have been performed in order to show the usefulness of bagging for this symbolic classifier...
In this contribution we explore the combination of bagging with random subspace and two variants of Battiti's mutual information feature selection methods to design fuzzy rule-based classification system ensembles. Besides, we consider a multicriteria genetic algorithm guided by the training error to select the component classifiers, in order to look for appropriate accuracy-complexity trade-offs...
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