Rafael Bello scite author profile

In this paper we propose a new model of ParticleSwarm Optimization called Two-Step PSO. The basic idea is to split the heuristic search performed by particles into two stages. We have studied the performance of this new algorithm for the Feature Selection problem by using the reduct concept of the Rough Set Theory. Experimental results obtained show that the Two-step approach improves over the PSO model in calculating reducts, with the same computational cost.

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ANN-QSAR model for selection of anticancer leads from structurally heterogeneous series of compounds

González-Dı́az

Bonet

Terán

et al. 2007

European Journal of Medicinal Chemistry

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Two-steps learning of Fuzzy Cognitive Maps for prediction and knowledge discovery on the HIV-1 drug resistance

Nápoles

Grau

Bello

et al. 2014

Expert Systems with Applications

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A model based on ant colony system and rough set theory to feature selection

Bello

Nowé

Caballero

et al. 2005

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Rafael Bello

SMOTE-RSB *: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory

Two-Step Particle Swarm Optimization to Solve the Feature Selection Problem

ANN-QSAR model for selection of anticancer leads from structurally heterogeneous series of compounds

Two-steps learning of Fuzzy Cognitive Maps for prediction and knowledge discovery on the HIV-1 drug resistance

A model based on ant colony system and rough set theory to feature selection

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