Title: An ensemble-based approach for image classification using voting classifier

Authors: Bhoopesh Singh Bhati; Achyut Shankar; Srishti Saxena; Tripti Saxena; M. Anbarasi; Manoj Kumar

Addresses: Department of Computer Science, UIE, Chandigarh University, Mohali, India ' Department of Computer Science, Amity University, Noida, India ' Amberdkar Institute of Advanced Communication Technologies and Research, Delhi, India ' Amberdkar Institute of Advanced Communication Technologies and Research, Delhi, India ' School of Computer Science and Engineering, VIT, Vellore, India ' School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India

Abstract: This paper presents a proposed scheme for image classification. Image classification is used in different areas to identify people, places, and objects images accurately. Image classification is considered to be a complicated process that may affect many factors. Many researchers work on image classification. There are many different image classification models, but the proposed scheme of using the voting classifier provides better results with high performance. The paper describes how the drawback of machine learning algorithms is overcome by using voting classifier, as it helps to improve the results by combining multiple machine learning algorithms. The experimentation is carried out on the fashion MNIST dataset. The fashion MNIST dataset is a benchmark for image classification. Three machine learning algorithms: K-nearest neighbour (KNN), random forest and decision tree, are used to evaluate the proposed scheme. The proposed scheme gives an accuracy of 87.39 %. The ensemble method is better as the individual accuracies of KNN, random forest, and decision tree are less than the proposed scheme, i.e., voting classifier.

Keywords: machine learning; image classification; voting classifier; ensemble learning.

DOI: 10.1504/IJMIC.2022.127099

International Journal of Modelling, Identification and Control, 2022 Vol.41 No.1/2, pp.87 - 97

Received: 04 Jul 2021
Accepted: 27 Oct 2021

Published online: 22 Nov 2022 *

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