Supervised fuzzy clustering for the identification of fuzzy classifiers
Pattern Recognition Letters
Computational Intelligence: for Engineering and Manufacturing
Computational Intelligence: for Engineering and Manufacturing
Implementing automated diagnostic systems for breast cancer detection
Expert Systems with Applications: An International Journal
Breast cancer diagnosis using least square support vector machine
Digital Signal Processing
Improving reservoirs using intrinsic plasticity
Neurocomputing
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
An expert system for detection of breast cancer based on association rules and neural network
Expert Systems with Applications: An International Journal
Support vector machines combined with feature selection for breast cancer diagnosis
Expert Systems with Applications: An International Journal
Breast mass classification based on cytological patterns using RBFNN and SVM
Expert Systems with Applications: An International Journal
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Kernel based support vector machine via semidefinite programming: Application to medical diagnosis
Computers and Operations Research
A novel feature selection approach for biomedical data classification
Journal of Biomedical Informatics
A linear learning method for multilayer perceptrons using least-squares
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
A combined neural network and decision trees model for prognosis of breast cancer relapse
Artificial Intelligence in Medicine
The multilayer perceptron as an approximation to a Bayes optimal discriminant function
IEEE Transactions on Neural Networks
On the biological plausibility of artificial metaplasticity
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
A prediction model to diabetes using artificial metaplasticity
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation: new challenges on bioinspired applications - Volume Part II
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A novel improvement in neural network training for pattern classification is presented in this paper. The proposed training algorithm is inspired by the biological metaplasticity property of neurons and Shannon's information theory. This algorithm is applicable to artificial neural networks (ANNs) in general, although here it is applied to a multilayer perceptron (MLP). During the training phase, the artificial metaplasticity multilayer perceptron (AMMLP) algorithm assigns higher values for updating the weights in the less frequent activations than in the more frequent ones. AMMLP achieves a more efficient training and improves MLP performance. The well-known and readily available Wisconsin Breast Cancer Database (WBCD) has been used to test the algorithm. Performance of the AMMLP was tested through classification accuracy, sensitivity and specificity analysis, and confusion matrix analysis. The results obtained by AMMLP are compared with the backpropagation algorithm (BPA) and other recent classification techniques applied to the same database. The best result obtained so far with the AMMLP algorithm is 99.63%.