Competitive voting-based multi-class prediction for ore selection

Zhang, Zelin, Liu, Ying, Hu, Qi, Zhang, Zhiwei and Liu, Yang 2020. Competitive voting-based multi-class prediction for ore selection. Presented at: 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), Virtual, 20-24 August 2020. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). IEEE, pp. 514-519. 10.1109/CASE48305.2020.9217017

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Abstract

Sensor-based intelligent sorting technology is a mineral separation technology with the merits of high-efficiency, energy-saving and water-saving. However, the prediction accuracy of conventional machine learning methods is unstable in multi-class selection of ores. The purpose of this study is to propose a competitive voting method to improve the multi-class prediction accuracy of ores in machine vision-based sorting system by combining the classification advantages of various machine learning methods. The operations of image segmentation, feature extraction and feature selection are presented to obtain the multi-class datasets. Three ones of traditional machine learning models with higher classification accuracies are used to establish competitive voting classification models. A case study using the image data of a gas coal shows the merits of the proposed approach. Results derived using this competitive voting approach reveal that it outperforms pre-existing approaches.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TJ Mechanical engineering and machinery T Technology > TN Mining engineering. Metallurgy
Publisher:	IEEE
ISBN:	9781728169040
Date of First Compliant Deposit:	12 June 2020
Date of Acceptance:	30 May 2020
Last Modified:	14 Oct 2020 08:29
URI:	https://orca.cardiff.ac.uk/id/eprint/132256

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