A Novel Autonomous Perceptron Model for Pattern Classification Applications

Entropy

Alaa Sagheer¹  Mohammed M. Abdelsamea²  Mohammed Zidan³ 

[1] College of Computer Science and Information Technology, King Faisal University, AlAhsa 31982, Saudi Arabia;Department of Mathematics, Faculty of Science, Assiut University, Assiut 71515, Egypt;University of Science and Technology, Zewail City of Science and Technology, October Gardens, 6th of October City, Giza 12578, Egypt;

关键词: machine learning;    pattern classification;    artificial neural networks;    quantum-inspired neural network;    soft computing;   

DOI  :  10.3390/e21080763

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown