Abstract
In this paper we are apply Artificial Metaplasticity MLP (MMLPs) to Breast Cancer Classification. Artificial Metaplasticity is a novel ANN training algorithm that gives more relevance to less frequent training patterns and subtract relevance to the frequent ones during training phase, achieving a much more efficient training, while at least maintaining the Multilayer Perceptron performance. Wisconsin Breast Cancer Database (WBCD) was used to train and test MMLPs. WBCD is a well-used database in machine learning, neural networks and signal processing. Experimental results show that MMLPs reach better accuracy than any other recent results.
His research has been partially supported by National (MICINN) and Madrid (CAM) Spanish institutions under the following projects: PTFNN (MCINN ref: AGL2006-12689/AGR). The author wishes to thank to The National Foundation of Science Technology and Innovation (FONACIT) of the Bolivariana Republic of Venezuela for its contribution in the development of his doctoral studies.
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Marcano-Cedeño, A., Buendía-Buendía, F.S., Andina, D. (2009). Breast Cancer Classification Applying Artificial Metaplasticity. In: Mira, J., Ferrández, J.M., Álvarez, J.R., de la Paz, F., Toledo, F.J. (eds) Bioinspired Applications in Artificial and Natural Computation. IWINAC 2009. Lecture Notes in Computer Science, vol 5602. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02267-8_6
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DOI: https://doi.org/10.1007/978-3-642-02267-8_6
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