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Mammographic Mass Classification Using Functional Link Neural Network with Modified Bee Firefly Algorithm

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Advances in Swarm Intelligence (ICSI 2016)

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Abstract

Functional Link Neural Network (FLNN) is a type of Higher Order Neural Networks (HONNs) known to have the modest architecture as compared to other multilayer feedforward networks. FLNN employs less tunable weights which make the learning method in the network less complicated. The standard learning method used in FLNN network is the Backpropagation (BP) learning algorithm. This method however, is prone to easily get trapped in local minima which affect the performance of the FLNN network. Thus an alternative learning method named modified Bee-Firefly (MBF) algorithm is proposed for FLNN. This paper presents the implementation FLNN trained with MBF on mammographic mass classification task. The result of the classification made by FLNN-MBF is compared with the standard FLNN-BP model to examine whether the MBF learning algorithm is capable of training the FLNN network and improve its performance for the task of classification.

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Acknowledgement

The authors would like to thank University Tun Hussein Onn Malaysia and Ministry of High Education (MOHE) for supporting this research under the Fundamental Research Grant Scheme (FRGS), vot. 1235.

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Correspondence to Yana Mazwin Mohmad Hassim .

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Hassim, Y.M.M., Ghazali, R. (2016). Mammographic Mass Classification Using Functional Link Neural Network with Modified Bee Firefly Algorithm. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2016. Lecture Notes in Computer Science(), vol 9712. Springer, Cham. https://doi.org/10.1007/978-3-319-41000-5_19

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  • DOI: https://doi.org/10.1007/978-3-319-41000-5_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40999-3

  • Online ISBN: 978-3-319-41000-5

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