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. LBAMN 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.
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Saavedra, E., Renners, I., Grauel, A., Convey, H.J., Razak, A. (2001). Gaussians-Based Hybrid System for Prediction and Classification. In: Reusch, B. (eds) Computational Intelligence. Theory and Applications. Fuzzy Days 2001. Lecture Notes in Computer Science, vol 2206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45493-4_50
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DOI: https://doi.org/10.1007/3-540-45493-4_50
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