Breast Cancer Classification Applying Artificial Metaplasticity

Authors:
Alexis Marcano-Cedeño;Fulgencio S. Buendía-Buendía;Diego Andina
Affiliations:
Universidad Politécnica de Madrid, Spain;Universidad Politécnica de Madrid, Spain;Universidad Politécnica de Madrid, Spain
Venue:
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
Year:
2009

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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.