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Meta-neuron learning based spiking neural classifier with time-varying weight model for credit scoring problem

Jeyasothy, A and Ramasamy, S and Sundaram, S (2021) Meta-neuron learning based spiking neural classifier with time-varying weight model for credit scoring problem. In: Expert Systems with Applications, 178 .

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Official URL: https://doi.org/10.1016/j.eswa.2021.114985


This paper presents a meta-neuron learning-based spiking neural classifier with a time-varying weight model (MeST). MeST is developed to handle the class imbalance in classification problems without any data preprocessing methods. Meta-neuron based learning algorithm in MeST uses normalized postsynaptic potentials (global information) and weight of the connection (local information) to determine the sensitivity modulation factor. This modulation factor determines the proportion of the weight update for a given set of presynaptic spikes. The weight update is then embedded in a Gaussian function to determine the time-varying weight update. The centre of the time-varying Gaussian function is determined by the presynaptic spike times. MeST is demonstrated on 10 benchmark datasets from the University of California, Irvine California machine learning repository and then applied to solve credit scoring using three real-world datasets. Performance studies show that the generalization ability of MeST is better than other spiking neural networks with constant weight model, despite having a simple architecture. Furthermore, compared to other non-spiking shallow machine learning classifiers, MeST is a slightly better model for classification using highly imbalanced datasets. This indicates the learnability of a stand-alone classifier on an imbalanced dataset can be increased by using time-varying weights. © 2021 Elsevier Ltd

Item Type: Journal Article
Publication: Expert Systems with Applications
Publisher: Elsevier Ltd
Additional Information: The copyright for this article belongs to Elsevier Ltd
Keywords: Analytical models; Classification (of information); Learning algorithms; Modulation; Neural networks; Supervised learning, Class imbalance; Credit scoring; Gaussian functions; Modulation factors; Neural classifiers; Presynaptic; Spiking neural network; Time-varying weight model; Time-varying weights; Weight update, Neurons
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited: 23 Jul 2021 09:54
Last Modified: 23 Jul 2021 09:54
URI: http://eprints.iisc.ac.in/id/eprint/68859

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