Support Vector Machine with Robust Low-Rank Learning for Multi-Label Classification Problems in the Steelmaking Process
In this paper, we present a novel support vector machine learning method for multi-label classification in the steelmaking process. The steelmaking process involves complicated physicochemical reactions. The end-point temperature is the key to the steelmaking process. According to the initial furnac...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-07-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/15/2659 |
| _version_ | 1797432786838093824 |
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| author | Qiang Li Chang Liu Qingxin Guo |
| author_facet | Qiang Li Chang Liu Qingxin Guo |
| author_sort | Qiang Li |
| collection | DOAJ |
| description | In this paper, we present a novel support vector machine learning method for multi-label classification in the steelmaking process. The steelmaking process involves complicated physicochemical reactions. The end-point temperature is the key to the steelmaking process. According to the initial furnace condition information, the end-point temperature can be predicted using a data-driven method. Based on the setting value of the temperature before tapping, multi-scale predicted errors of the end-point temperature can be calculated and divided into different ranges. The quality evaluation problem can be attributed to the multi-label classification problem of molten steel quality. To solve the classification problem, considering that it is difficult to capture nonlinear relationships between the input and output in linear models, we propose a novel support vector machine with robust low-rank learning, which has the characteristics of class imbalance without label correlations; a low-rank constraint is used to deal with high-order label correlations in low-dimensional space. Furthermore, we derive an accelerated proximal gradient algorithm and then extend it to handle the nonlinear multi-label classifiers. To validate the proposed model, experiments are conducted with real data from a practical steelmaking problem. The results show that the proposed model can effectively solve the multi-label classification problem in industrial production. To evaluate the proposed approach as a general classification approach, we test it on multi-label classification benchmark datasets. The results illustrate that the proposed approach performs better than other state-of-the-art approaches across different scenarios. |
| first_indexed | 2024-03-09T10:07:41Z |
| format | Article |
| id | doaj.art-9dc314e6bf2243d9bfbc03056a3e9856 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T10:07:41Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-9dc314e6bf2243d9bfbc03056a3e98562023-12-01T23:02:08ZengMDPI AGMathematics2227-73902022-07-011015265910.3390/math10152659Support Vector Machine with Robust Low-Rank Learning for Multi-Label Classification Problems in the Steelmaking ProcessQiang Li0Chang Liu1Qingxin Guo2Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry, Shenyang 110819, ChinaKey Laboratory of Data Analytics and Optimization for Smart Industry, Northeastern University, Ministry of Education, Shenyang 110819, ChinaNational Frontiers Science Center for Industrial Intelligence and Systems Optimization, Northeastern University, Shenyang 110819, ChinaIn this paper, we present a novel support vector machine learning method for multi-label classification in the steelmaking process. The steelmaking process involves complicated physicochemical reactions. The end-point temperature is the key to the steelmaking process. According to the initial furnace condition information, the end-point temperature can be predicted using a data-driven method. Based on the setting value of the temperature before tapping, multi-scale predicted errors of the end-point temperature can be calculated and divided into different ranges. The quality evaluation problem can be attributed to the multi-label classification problem of molten steel quality. To solve the classification problem, considering that it is difficult to capture nonlinear relationships between the input and output in linear models, we propose a novel support vector machine with robust low-rank learning, which has the characteristics of class imbalance without label correlations; a low-rank constraint is used to deal with high-order label correlations in low-dimensional space. Furthermore, we derive an accelerated proximal gradient algorithm and then extend it to handle the nonlinear multi-label classifiers. To validate the proposed model, experiments are conducted with real data from a practical steelmaking problem. The results show that the proposed model can effectively solve the multi-label classification problem in industrial production. To evaluate the proposed approach as a general classification approach, we test it on multi-label classification benchmark datasets. The results illustrate that the proposed approach performs better than other state-of-the-art approaches across different scenarios.https://www.mdpi.com/2227-7390/10/15/2659steelmaking processmulti-label classificationsupport vector machinerobust low-rank learningpractical problem |
| spellingShingle | Qiang Li Chang Liu Qingxin Guo Support Vector Machine with Robust Low-Rank Learning for Multi-Label Classification Problems in the Steelmaking Process Mathematics steelmaking process multi-label classification support vector machine robust low-rank learning practical problem |
| title | Support Vector Machine with Robust Low-Rank Learning for Multi-Label Classification Problems in the Steelmaking Process |
| title_full | Support Vector Machine with Robust Low-Rank Learning for Multi-Label Classification Problems in the Steelmaking Process |
| title_fullStr | Support Vector Machine with Robust Low-Rank Learning for Multi-Label Classification Problems in the Steelmaking Process |
| title_full_unstemmed | Support Vector Machine with Robust Low-Rank Learning for Multi-Label Classification Problems in the Steelmaking Process |
| title_short | Support Vector Machine with Robust Low-Rank Learning for Multi-Label Classification Problems in the Steelmaking Process |
| title_sort | support vector machine with robust low rank learning for multi label classification problems in the steelmaking process |
| topic | steelmaking process multi-label classification support vector machine robust low-rank learning practical problem |
| url | https://www.mdpi.com/2227-7390/10/15/2659 |
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