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
Interval computing in neural networks: one layer interval neural networks
CIT'04 Proceedings of the 7th international conference on Intelligent Information Technology
Genetic interval neural networks for granular data regression
Information Sciences: an International Journal
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Granular data offer an interesting vehicle of representing the available information in problems where uncertainty, inaccuracy, variability or, in general, 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. The modeling capabilities of the proposed MLP are illustrated by means of its application to both synthetic and real world datasets.