Leak Detection in Water Pipes Based on Maximum Entropy Version of Least Square Twin K-Class Support Vector Machine
Numerous novel improved support vector machine (SVM) methods are used in leak detection of water pipelines at present. The least square twin K-class support vector machine (LST-KSVC) is a novel simple and fast multi-classification method. However, LST-KSVC has a non-negligible drawback that it assig...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/10/1247 |
| _version_ | 1797514646681288704 |
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| author | Mingyang Liu Jin Yang Wei Zheng |
| author_facet | Mingyang Liu Jin Yang Wei Zheng |
| author_sort | Mingyang Liu |
| collection | DOAJ |
| description | Numerous novel improved support vector machine (SVM) methods are used in leak detection of water pipelines at present. The least square twin K-class support vector machine (LST-KSVC) is a novel simple and fast multi-classification method. However, LST-KSVC has a non-negligible drawback that it assigns the same classification weights to leak samples, including outliers that affect classification, these outliers are often situated away from the main leak samples. To overcome this shortcoming, the maximum entropy (MaxEnt) version of the LST-KSVC is proposed in this paper, called the MLT-KSVC algorithm. In this classification approach, classification weights of leak samples are calculated based on the MaxEnt model. Different sample points are assigned different weights: large weights are assigned to primary leak samples and outliers are assigned small weights, hence the outliers can be ignored in the classification process. Leak recognition experiments prove that the proposed MLT-KSVC algorithm can reduce the impact of outliers on the classification process and avoid the misclassification color block drawback in linear LST-KSVC. MLT-KSVC is more accurate compared with LST-KSVC, TwinSVC, TwinKSVC, and classic Multi-SVM. |
| first_indexed | 2024-03-10T06:34:33Z |
| format | Article |
| id | doaj.art-4b97a16ae42c4ae5aeb98d1ed2823ba3 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T06:34:33Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-4b97a16ae42c4ae5aeb98d1ed2823ba32023-11-22T18:10:03ZengMDPI AGEntropy1099-43002021-09-012310124710.3390/e23101247Leak Detection in Water Pipes Based on Maximum Entropy Version of Least Square Twin K-Class Support Vector MachineMingyang Liu0Jin Yang1Wei Zheng2Key Laboratory of Optoelectronic Technology & Systems (Ministry of Education), Department of Optoelectronic Engineering, Chongqing University, Chongqing 400044, ChinaKey Laboratory of Optoelectronic Technology & Systems (Ministry of Education), Department of Optoelectronic Engineering, Chongqing University, Chongqing 400044, ChinaKey Laboratory of Optoelectronic Technology & Systems (Ministry of Education), Department of Optoelectronic Engineering, Chongqing University, Chongqing 400044, ChinaNumerous novel improved support vector machine (SVM) methods are used in leak detection of water pipelines at present. The least square twin K-class support vector machine (LST-KSVC) is a novel simple and fast multi-classification method. However, LST-KSVC has a non-negligible drawback that it assigns the same classification weights to leak samples, including outliers that affect classification, these outliers are often situated away from the main leak samples. To overcome this shortcoming, the maximum entropy (MaxEnt) version of the LST-KSVC is proposed in this paper, called the MLT-KSVC algorithm. In this classification approach, classification weights of leak samples are calculated based on the MaxEnt model. Different sample points are assigned different weights: large weights are assigned to primary leak samples and outliers are assigned small weights, hence the outliers can be ignored in the classification process. Leak recognition experiments prove that the proposed MLT-KSVC algorithm can reduce the impact of outliers on the classification process and avoid the misclassification color block drawback in linear LST-KSVC. MLT-KSVC is more accurate compared with LST-KSVC, TwinSVC, TwinKSVC, and classic Multi-SVM.https://www.mdpi.com/1099-4300/23/10/1247leak detectionoutliersLST-KSVCmaximum entropyMLT-KSVC |
| spellingShingle | Mingyang Liu Jin Yang Wei Zheng Leak Detection in Water Pipes Based on Maximum Entropy Version of Least Square Twin K-Class Support Vector Machine Entropy leak detection outliers LST-KSVC maximum entropy MLT-KSVC |
| title | Leak Detection in Water Pipes Based on Maximum Entropy Version of Least Square Twin K-Class Support Vector Machine |
| title_full | Leak Detection in Water Pipes Based on Maximum Entropy Version of Least Square Twin K-Class Support Vector Machine |
| title_fullStr | Leak Detection in Water Pipes Based on Maximum Entropy Version of Least Square Twin K-Class Support Vector Machine |
| title_full_unstemmed | Leak Detection in Water Pipes Based on Maximum Entropy Version of Least Square Twin K-Class Support Vector Machine |
| title_short | Leak Detection in Water Pipes Based on Maximum Entropy Version of Least Square Twin K-Class Support Vector Machine |
| title_sort | leak detection in water pipes based on maximum entropy version of least square twin k class support vector machine |
| topic | leak detection outliers LST-KSVC maximum entropy MLT-KSVC |
| url | https://www.mdpi.com/1099-4300/23/10/1247 |
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