Abstract
Functional Link Neural Network (FLNN) has become as an important tool used in classification tasks due to its modest architecture. FLNN requires less tunable weights for training as compared to the standard multilayer feed forward network. Since FLNN uses Backpropagation algorithm as the standard learning scheme, the method however prone to get trapped in local minima which affect its classification performance. This paper proposed the implementation of modified Bee-Firefly algorithm as an alternative learning scheme for FLNN for the task of mammographic mass classification. The implementation of the proposed learning scheme demonstrated that the FLNN can successfully perform the classification task with better accuracy result on unseen data.
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Hassim, Y.M.M., Ghazali, R. (2016). Improving Functional Link Neural Network Learning Scheme for Mammographic Classification. In: Bassis, S., Esposito, A., Morabito, F., Pasero, E. (eds) Advances in Neural Networks. WIRN 2015. Smart Innovation, Systems and Technologies, vol 54. Springer, Cham. https://doi.org/10.1007/978-3-319-33747-0_21
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DOI: https://doi.org/10.1007/978-3-319-33747-0_21
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