Gaussians-Based Hybrid System for Prediction and Classification

Authors:
Ernesto Saavedra;Ingo Renners;Adolf Grauel;Harold J. Convey;A. Razak
Affiliations:
-;-;-;-;-
Venue:
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Year:
2001

Citing 3
Cited 0

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Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence

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Abstract

We propose a hybrid model based on Genetic Algorithms (GA), Lattice Based Associative Memory Networks (LB-AMN) and Radial Basis Function Networks (RBFN) for the solution of prediction and classification problems. LB-AMN and RBFN have as basis in their structure a type of asymmetric radial basis function (RBF) which results from the combination of two Gaussian functions. In the first sections we describe the mathematical models used to build the hybrid system. Afterwards, we apply the model to the problem of breast cancer and toxicity prediction. In both cases, the obtained results were better than the ones obtained using other approaches. Finally, some conclusions are given.