Proceedings of the third international conference on Genetic algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Knowledge-Based Clustering: From Data to Information Granules
Knowledge-Based Clustering: From Data to Information Granules
A Reflex Fuzzy Min Max Neural Network for Granular Data Classification
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
iMLP: Applying Multi-Layer Perceptrons to Interval-Valued Data
Neural Processing Letters
A New Interval-Genetic Algorithm
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 04
A comparison of selection schemes used in evolutionary algorithms
Evolutionary Computation
Granular computing: Models and applications
International Journal of Intelligent Systems - Granular Computing: Models and Applications
Genetic algorithm for asymmetric traveling salesman problem with imprecise travel times
Journal of Computational and Applied Mathematics
Granular data regression with neural networks
WILF'11 Proceedings of the 9th international conference on Fuzzy logic and applications
Interval computing in neural networks: one layer interval neural networks
CIT'04 Proceedings of the 7th international conference on Intelligent Information Technology
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Granular data and granular models offer an interesting tool for representing data in problems involving uncertainty, inaccuracy, variability and subjectivity have to be taken into account. In this paper, we deal with a particular type of information granules, namely interval-valued data. We propose a multilayer perceptron (MLP) to model interval-valued input-output mappings. The proposed MLP comes with interval-valued weights and biases, and is trained using a genetic algorithm designed to fit data with different levels of granularity. In the evolutionary optimization, two implementations of the objective function, based on a numeric-valued and an interval-valued network error, respectively, are discussed and compared. The modeling capabilities of the proposed MLP are illustrated by means of its application to both synthetic and real world datasets.