Evaluation of dynamic behavior forecasting parameters in the process of transition rule induction of unidimensional cellular automata

Weinert, Wagner Rodrigo; Lopes, Heitor Silvério

doi:10.1016/j.biosystems.2009.08.002

Cited by 3 publications

(1 citation statement)

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“…Consequently, rule induction with using EC requires intensive computation, and so, a parallel processing architecture is necessary to reduce the overall processing time (Weinert and Lopes, 2010). In the process of rule induction -see Section 2.2, the computation of the fitness function consists in the evaluation of set of instances (training set) as many times as the product of the number of individuals by the number of generations.…”

Section: Introductionmentioning

confidence: 99%

Data mining with a parallel rule induction system based on gene expression programming

Weinert

Lopes

2011

IJICA

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A parallel rule induction system based on gene expression programming (GEP) is reported in this paper. The system was developed for data classification. The parallel processing environment was implemented on a cluster using a message-passing interface. A master-slave GEP was implemented according to the Michigan approach for representing a solution for a classification problem. A multiple master-slave system (islands) was implemented in order to observe the co-evolution effect. Experiments were done with ten datasets, and algorithms were systematically compared with C4.5. Results were analysed from the point of view of a multi-objective problem, taking into account both predictive accuracy and comprehensibility of induced rules. Overall results indicate that the proposed system achieves better predictive accuracy with shorter rules, when compared with C4.5.

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Section: Introductionmentioning

confidence: 99%