A Novel Quantum-inspired Fuzzy Based Neural Network for Data Classification

Patel, OP; Bharill, N; Tiwari, A; Prasad, M

A Novel Quantum-inspired Fuzzy Based Neural Network for Data Classification

Patel, OP Bharill, N Tiwari, A Prasad, M

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Emerging Topics in Computing, 2019
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Patel, OP	en_US
dc.contributor.author	Bharill, N	en_US
dc.contributor.author	Tiwari, A	en_US
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	IEEE Transactions on Emerging Topics in Computing, 2019	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132456
dc.description.abstract	IEEE The performance of the neural network (NN) depends on the various parameters such as structure, initial weight, number of hidden layer neurons, and learning rate. The improvement in classification performance of NN without changing its structure is a challenging issue. This paper proposes a novel learning model called Quantum-inspired Fuzzy Based Neural Network (Q-FNN) to solve two-class classification problems. In the proposed model, NN architecture is formed constructively by adding neurons in the hidden layer and learning is performed using the concept of Fuzzy c-Means (FCM) clustering, where the fuzziness parameter (m) is evolved using the quantum computing concept. The quantum computing concept provides a large search space for a selection of m, which helps in finding the optimal weights and also optimizes the network architecture. This paper also proposes a modified step activation function for the formation of hidden layer neurons, which handles the overlapping samples belong to different class regions. The performance of the proposed Q-FNN model is superior and competitive with the state-of-the-art methods in terms of accuracy, sensitivity, and specificity on 15 real-world benchmark datasets.	en_US
dc.relation.ispartof	IEEE Transactions on Emerging Topics in Computing	en_US
dc.relation.isbasedon	10.1109/TETC.2019.2901272	en_US
dc.title	A Novel Quantum-inspired Fuzzy Based Neural Network for Data Classification	en_US
dc.type	Journal Article
utslib.for	0805 Distributed Computing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

IEEE The performance of the neural network (NN) depends on the various parameters such as structure, initial weight, number of hidden layer neurons, and learning rate. The improvement in classification performance of NN without changing its structure is a challenging issue. This paper proposes a novel learning model called Quantum-inspired Fuzzy Based Neural Network (Q-FNN) to solve two-class classification problems. In the proposed model, NN architecture is formed constructively by adding neurons in the hidden layer and learning is performed using the concept of Fuzzy c-Means (FCM) clustering, where the fuzziness parameter (m) is evolved using the quantum computing concept. The quantum computing concept provides a large search space for a selection of m, which helps in finding the optimal weights and also optimizes the network architecture. This paper also proposes a modified step activation function for the formation of hidden layer neurons, which handles the overlapping samples belong to different class regions. The performance of the proposed Q-FNN model is superior and competitive with the state-of-the-art methods in terms of accuracy, sensitivity, and specificity on 15 real-world benchmark datasets.

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http://hdl.handle.net/10453/132456