Granular Data Regression with Neural Networks

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.