A Binary Bat Inspired Algorithm for the Classification of Breast Cancer Data
Doreswamy, and U. M.
International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI) 5 (3): 21 (August 2016)

Advancement in information and technology has made a major impact on medical science where theresearchers come up with new ideas for improving the classification rate of various diseases. Breast canceris one such disease killing large number of people around the world. Diagnosing the disease at its earliestinstance makes a huge impact on its treatment. The authors propose a Binary Bat Algorithm (BBA) based Feedforward Neural Network (FNN) hybrid model, where the advantages of BBA and efficiency of FNN isexploited for the classification of three benchmark breast cancer datasets into malignant and benign cases.Here BBA is used to generate a V-shaped hyperbolic tangent function for training the network and a fitnessfunction is used for error minimization. FNNBBA based classification produces 92.61% accuracy for training data and 89.95% for testing data.
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