Feature Selection through Dynamic Mesh Optimization

Bello, Rafael; Puris, Amilkar; Falcón, Rafael; Gómez, Yudel

doi:10.1007/978-3-540-85920-8_43

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…For solving this complex combinatorial task the framework FCM TOOL uses 8 discrete methods, which includes a BinaryCoded Genetic Algorithm [19], Ant Colony Optimizers [26] and variants of Variable Mesh Optimization [27].…”

Section: B Optimizing the Network Topologymentioning

confidence: 99%

A computational tool for simulation and learning of Fuzzy Cognitive Maps

Nápoles

Grau

Bello

et al. 2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Abstract-During the last decade Fuzzy Cognitive Maps (FCM)have become a useful tool for solving unstructured problems. In a few words they could be defined as Recurrent Neural Networks for simulating complex systems, where neurons denote concepts, objects or entities of the investigated system. Normally FCM are entirely designed using the best knowledge of a group of experts in a given domain, so frequently learning algorithms for tuning the model parameters are required. Despite the theoretical advances in such fields, the lack of a suitable computational framework for handling FCM-based systems is still an open problem. This paper introduces a novel tool for designing and simulating FCM which gathers several learning algorithms for adjusting the introduced parameters. More specifically, the framework includes supervised and unsupervised learning algorithms for computing the causal weights, algorithms for optimizing the network topology in large FCM (without losing significant information) and also methods for improving the global convergence on continuous FCM. It should be stated that these algorithms are oriented to prediction tasks, but they could be easily extended to other fields. Index Terms -FCM, prediction problems, modeling, learning algorithms, computational framework.

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Section: B Optimizing the Network Topologymentioning

confidence: 99%

A computational tool for simulation and learning of Fuzzy Cognitive Maps

Nápoles

Grau

Bello

et al. 2015

2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…We want to elaborate now on a novel optimization technique called "Dynamic Mesh Optimization" (DMO) [5] which follows some patterns already present in earlier evolutionary approaches but provides a unique framework for managing both discrete and continuous optimization problems.…”

Section: Dynamic Mesh Optimization In Feature Selectionmentioning

confidence: 99%

Rough Sets and Evolutionary Computation to Solve the Feature Selection Problem

Bello

Gómez

Caballero

et al.

Studies in Computational Intelligence

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Summary. The feature selection problem has been usually addressed through heuristic approaches given its significant computational complexity. In this context, evolutionary techniques have drawn the researchers' attention owing to their appealing optimization capabilities. In this chapter, promising results achieved by the authors in solving the feature selection problem through a joint effort between rough set theory and evolutionary computation techniques are reviewed. In particular, two new heuristic search algorithms are introduced, i.e. Dynamic Mesh Optimization and another approach which splits the search process carried out by swarm intelligence methods.

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