Abstract
Inspired by the brain working mechanism, the spiking neural networks has proven the capability of revealing significant association between different variables spike behavior during an event. The combination of the capability of SNN to produce personalised model has allowed high-precision for data classification. The exiting accuracy of weighted k-nearest neighbors classifier being used in the spiking neural networks architecture, noticeably can be further improved by implementing fuzzy-weights on the features, therefore allowing data to be classified more precisely to the high-impacting features. Simulation has been done by using three classifiers—Multi-layer Perceptron, weighted k-nearest neighbors, and Fuzzy-weighted k-nearest neighbors (FwkNN) using a real-world flood case study dataset and two benchmark dataset. Based on the result using the Kuala Krai Rainfall Dataset, FwkNN classifier has improved accuracy by 3.48% and 3.57% for 3-days earlier and 1-day earlier classification respectively. As compared to, FwkNN classifier has proven the capability to reduce misclassification and increase the accuracy of dataset classification.
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The authors would like to thank Universiti Tun Hussein Onn Malaysia for supporting this paper publication.
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Abdullah, M.H.A., Othman, M., Kasim, S., Saharuddin, S.S., Mohamed, S.A. (2020). A Spiking Neural Networks Model with Fuzzy-Weighted k-Nearest Neighbour Classifier for Real-World Flood Risk Assessment. In: Ghazali, R., Nawi, N., Deris, M., Abawajy, J. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2020. Advances in Intelligent Systems and Computing, vol 978. Springer, Cham. https://doi.org/10.1007/978-3-030-36056-6_22
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