Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-23713-3_22 | WILF |
Keywords | Field | DocType |
different level,neural network,genetic algorithm,granular data regression,available information,information granule,interval-valued input-output mapping,granular data,interesting vehicle,proposed mlp,interval-valued data,interval-valued weight | Data mining,Function approximation,Computer science,Granular computing,Multilayer perceptron,Artificial intelligence,Symbolic data analysis,Granularity,Interval arithmetic,Artificial neural network,Machine learning,Genetic algorithm | Conference |
Citations | PageRank | References |
3 | 0.42 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mario G. C. A. Cimino | 1 | 268 | 29.52 |
| Beatrice Lazzerini | 2 | 715 | 45.56 |
| Francesco Marcelloni | 3 | 1404 | 91.43 |
| W. Pedrycz | 4 | 13966 | 1005.85 |