Zhang, Zelin, Liu, Ying  ORCID: https://orcid.org/0000-0001-9319-5940, Hu, Qi, Zhang, Zhiwei and Liu, Yang ORCID: https://orcid.org/0000-0001-9319-5940
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: | 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: | 07 Nov 2022 10:26 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/132256 |
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