Fuzzy heterogeneous neurons for imprecise classification problems
Visualitza/Obre
10.1002/(SICI)1098-111X(200003)15:3<265::AID-INT7>3.0.CO;2-I
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/182212
Tipus de documentArticle
Data publicació2000-02
EditorWiley
Condicions d'accésAccés obert
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Abstract
In the classical neuron model, inputs are continuous real-valued quantities. However, in many important domains from the real world, objects are described by a mixture of continuous and discrete variables, usually containing missing information and uncertainty. In this paper, a general class of neuron models accepting heterogeneous inputs in the form of mixtures of continuous (crisp and/or fuzzy) and discrete quantities admitting missing data is presented. From these, several particular models can be derived as instances and different neural architectures constructed with them. Such models deal in a natural way with problems for which information is imprecise or even missing. Their possibilities in classification and diagnostic problems are here illustrated by experiments with data from a real-world domain in the field of environmental studies. These experiments show that such neurons can both learn and classify complex data very effectively in the presence of uncertain information.
CitacióValdés, J.; Belanche, L.; Alquézar, R. Fuzzy heterogeneous neurons for imprecise classification problems. "International journal of intelligent systems", Febrer 2000, vol. 15, núm. 3, p. 265-276.
ISSN0884-8173
Col·leccions
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