Liu Mingyang, Yang Jin, Zheng Wei
Key Laboratory of Optoelectronic Technology & Systems (Ministry of Education), Department of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China.
Entropy (Basel). 2021 Sep 25;23(10):1247. doi: 10.3390/e23101247.
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.
目前，许多新颖的改进型支持向量机（SVM）方法被用于输水管道的泄漏检测。最小二乘孪生K类支持向量机（LST-KSVC）是一种新颖的简单快速多分类方法。然而，LST-KSVC有一个不可忽视的缺点，即它给泄漏样本赋予相同的分类权重，包括那些影响分类的离群值，这些离群值往往远离主要的泄漏样本。为克服这一缺点，本文提出了LST-KSVC的最大熵（MaxEnt）版本，称为MLT-KSVC算法。在这种分类方法中，基于MaxEnt模型计算泄漏样本的分类权重。不同的样本点被赋予不同的权重：主要泄漏样本被赋予较大权重，离群值被赋予较小权重，因此在分类过程中可以忽略离群值。泄漏识别实验证明，所提出的MLT-KSVC算法能够减少离群值对分类过程的影响，避免线性LST-KSVC中误分类色块的缺点。与LST-KSVC、TwinSVC、TwinKSVC和经典的多支持向量机相比，MLT-KSVC更准确。
