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A Novel Autonomous Perceptron Model for Pattern Classification Applications

1
College of Computer Science and Information Technology, King Faisal University, AlAhsa 31982, Saudi Arabia
2
Center for Artificial Intelligence and Robotics (CAIRO), Faculty of Science, Aswan University, Aswan 81528, Egypt
3
University of Science and Technology, Zewail City of Science and Technology, October Gardens, 6th of October City, Giza 12578, Egypt
4
Department of Mathematics, Faculty of Science, Assiut University, Assiut 71515, Egypt
5
School of Computer Science, University of Nottingham, Nottingham NG8 1BB, UK
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(8), 763; https://doi.org/10.3390/e21080763
Received: 29 May 2019 / Revised: 30 July 2019 / Accepted: 30 July 2019 / Published: 6 August 2019
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Abstract

Pattern classification represents a challenging problem in machine learning and data science research domains, especially when there is a limited availability of training samples. In recent years, artificial neural network (ANN) algorithms have demonstrated astonishing performance when compared to traditional generative and discriminative classification algorithms. However, due to the complexity of classical ANN architectures, ANNs are sometimes incapable of providing efficient solutions when addressing complex distribution problems. Motivated by the mathematical definition of a quantum bit (qubit), we propose a novel autonomous perceptron model (APM) that can solve the problem of the architecture complexity of traditional ANNs. APM is a nonlinear classification model that has a simple and fixed architecture inspired by the computational superposition power of the qubit. The proposed perceptron is able to construct the activation operators autonomously after a limited number of iterations. Several experiments using various datasets are conducted, where all the empirical results show the superiority of the proposed model as a classifier in terms of accuracy and computational time when it is compared with baseline classification models. View Full-Text
Keywords: machine learning; pattern classification; artificial neural networks; quantum-inspired neural network; soft computing machine learning; pattern classification; artificial neural networks; quantum-inspired neural network; soft computing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Sagheer, A.; Zidan, M.; Abdelsamea, M.M. A Novel Autonomous Perceptron Model for Pattern Classification Applications. Entropy 2019, 21, 763.

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