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Fuzzy multi-class classifier based on support vector data description and improved PCM

EXPERT SYSTEMS WITH APPLICATIONS 2009年第5期36卷 8714-8718页

作者： Zhang, Yong Chi, Zhong-Xian Li, Ke-Qiu Liaoning Normal Univ Dept Comp Dalian 116081 Liaoning Prov Peoples R China Dalian Univ Technol Dept Comp Sci & Engn Dalian 116024 Peoples R China

in this paper, a novel fuzzy classifier for multi-classification problems, based on support vector data description (SVDD) and improved PCM, is proposed. The proposed method is the robust version of SVDD by assigning a weight to each data point, which represents fuzzy membership degree of the cluster computed by the improved PCM method. Accordingly, this paper presents the multi-classification algorithm based on the robust weighted SVDD, and gives the simple classification rule. Experimental results show that the proposed method can reduce the effect of outliers and yield higher classification rate. (c) 2008 Elsevier Ltd. All rights reserved.

关键词： support vector data description Possibilistic c-means algorithm Minimum enclosing sphere Classifier SVM

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Detection of Fabric Defects by Auto-Regressive Spectral Analysis and support vector data description

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TEXTILE RESEARCH JOURNAL 2010年第7期80卷 579-589页

作者： Bu, H. -G. Huang, X. -B. Wang, J. Chen, X. Donghua Univ Coll Text Shanghai 201620 Peoples R China Minist Educ Key Lab Text Sci & Technol Shanghai 201620 Peoples R China

For the purpose of realizing fast and effective detection of defects in woven fabric, and in consideration of the inherent characteristics of fabric texture, i.e., periodicity and orientation, a new approach for fabric texture analysis, based on the modern spectral analysis of a time series rather than the classical spectral analysis of an image, is proposed in this paper. Traditionally, a power spectral estimated by a two-dimensional Fast Fourier transformation (FFT) is usually employed in the detection of fabric defects, which involves a large computational complexity and a relatively low accuracy of spectral estimation. To this effect, this paper makes a one-dimensional power spectral density (PSD) analysis of the fabric image via a Burg-algorithm-based Auto-Regressive (AR) spectral estimation model, and accordingly extracts features capable of effectively differentiating normal textures from defective ones. A support vector data description is adopted as a detector in order to deal with defect detection, a typical task of one-class classification. Experimental results for the detection of defects from several fabric collections with different texture backgrounds indicate that a low false alarm rate and a low missing rate can be simultaneously obtained with less computational complexity. Comparison of the detection results between the AR model and the FFT method confirms the superiority of the proposed method.

关键词： fabric defect detection time series analysis AR spectral estimation one-class classification support vector data description

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Theoretical analysis for solution of support vector data description

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NEURAL NETWORKS 2011年第4期24卷 360-369页

作者： Wang, Xiaoming Chung, Fu-lai Wang, Shitong Jiangnan Univ Sch Digital Media Wuxi Jiangsu Peoples R China Jiangnan Univ Sch Informat Wuxi Jiangsu Peoples R China HongKong Polytech Univ Dept Comp Hong Kong Hong Kong Peoples R China

As we may know well, uniqueness of the support vector Machines (SVM) solution has been solved. However, whether support vector data description (SVDD), another best-known machine learning method, has a unique solution or not still remains unsolved. Due to the fact that the primal optimization of SVDD is not a convex programming problem, it is difficult for us to theoretically analyze the SVDD solution in an analogous way to SVM. In this paper, we concentrate on the theoretical analysis for the solution to the primal optimization problem of SVDD. We first reformulate equivalently the primal optimization problem of SVDD into a convex programming problem, and then prove that the optimal solution with respect to the sphere center is unique, derive the necessary and sufficient conditions of non-uniqueness of the optimal solution with respect to the sphere radius in the primal optimization problem of SVDD. Moreover, we also explore the property of the SVDD solution from the perspective of the SVDD dual form. Furthermore, according to the geometric interpretation of SVDD, a method of computing the sphere radius is proposed when the optimal solution with respect to the sphere radius in the primal optimization problem is non-unique. Finally, we have several examples to illustrate these findings. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Kernel methods support vector data description Convex optimization Uniqueness

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Batch process monitoring based on support vector data description method

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JOURNAL OF PROCESS CONTROL 2011年第6期21卷 949-959页

作者： Ge, Zhiqiang Gao, Furong Song, Zhihuan Hong Kong Univ Sci & Technol Dept Chem & Biomol Engn Kowloon Hong Kong Peoples R China Zhejiang Univ Inst Ind Proc Control State Key Lab Ind Control Technol Hangzhou 310027 Zhejiang Peoples R China

Process monitoring can be considered as a one-class classification problem, the aim of which is to differentiate the normal data samples from the faulty ones. This paper introduces an efficient one-class classification method for batch process monitoring, which is called support vector data description (SVDD). Different from the traditional data description method such as principal component analysis (PCA) and partial least squares (PLS), SVDD has no Gaussian assumption of the process data, and is also effective for nonlinear process modeling. Furthermore, SVDD only incorporates a quadratic optimization step, which makes it easy for practical implementation. Based on the basic SVDD batch process monitoring approach, the method is further extended to multiphase and multimode batch processes. Two case studies are provided to evaluate the monitoring performance of the proposed methods. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Batch process monitoring Non-Gaussian Nonlinear Multiphase Multimode support vector data description

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An assessment for the conditional performance of an support vector data description (SVDD)-based chart

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2022年第5期38卷 2256-2272页

作者： Tang, Anan Castagliola, Philippe Hu, Xuelong Xie, Fupeng Nanjing Univ Posts & Telecommun Nanjing Peoples R China Informat Ind Integrat Innovat & Emergency Managem Key Res Base Philosophy & Social Sci Jiangsu Nanjing Peoples R China Univ Nantes Nantes France LS2N UMR CNRS 6004 Nantes France Nanjing Univ Sci & Technol Nanjing Peoples R China

The usual practice in Statistical Process Monitoring (SPM) techniques assumes that the data distribution is known and the related parameters are accurately estimated. In practice, the underlying distribution and its parameters are rarely known, and control charts need to be constructed with parameters being estimated. Such issues have recently received an increasing attention in evaluating the properties of both parametric and nonparametric charts. However, the same study is seldom conducted for the control charts based on the data-driven tools. In this paper, we investigated the in-control performance of a nonparametric control chart based on the support vector data description (SVDD) theory. More specifically, we discuss the conditional effect of the training Phase-I samples on the Phase-II efficiency when different distributions are considered. Simulation results show that the conditional performance of the SVDD-based chart can be strongly affected by the Phase-I samples. It this situation, adjusted control limits with a specific number of available training sample is suggested.

关键词： conditional performance control chart Phase-I Phase-II support vector data description

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Two-class support vector data description

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PATTERN RECOGNITION 2011年第2期44卷 320-329页

作者： Huang, Guangxin Chen, Huafu Zhou, Zhongli Yin, Feng Guo, Ke Univ Elect Sci & Technol China Sch Math Sci Sch Life Sci & Technol Key Lab Neuroinformat Minist Educ Chengdu 610054 Peoples R China Chengdu Univ Technol Sch Informat & Management Key Lab Geomath Sichuan Prov Chengdu 610059 Peoples R China Sichuan Univ Sci & Engn Sch Sci Zigong 643000 Peoples R China

support vector data description (SVDD) is a data description method that can give the target data set a spherically shaped description and be used to outlier detection or classification. In real life the target data set often contains more than one class of objects and each class of objects need to be described and distinguished simultaneously. In this case, traditional SVDD can only give a description for the target data set, regardless of the differences between different target classes in the target data set, or give a description for each class of objects in the target data set. In this paper, an improved support vector data description method named two-class support vector data description (TC-SVDD) is presented. The proposed method can give each class of objects in the target data set a hypersphere-shaped description simultaneously if the target data set contains two classes of objects. The characteristics of the improved support vector data descriptions are discussed. The results of the proposed approach on artificial and actual data show that the proposed method works quite well on the 3-class classification problem with one object class being undersampled severely. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： support vector data description D-SVDD TC-SVDD One-class classification

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Independent component analysis-based non-Gaussian process monitoring with preselecting optimal components and support vector data description

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2014年第11期52卷 3273-3286页

作者： Jiang, Qingchao Yan, Xuefeng Lv, Zhaomin Guo, Meijin E China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China E China Univ Sci & Technol State Key Lab Bioreactor Engn Shanghai 200237 Peoples R China

Independent component analysis (ICA)-based process monitoring methods have rapidly progressed, but independent components (ICs) selection remains an open question. Subjective ICs selection would lead to useful information dispersion and affect the ICA monitoring performance. A novel ICA-based method integrated with preselecting optimal components and support vector machine data description (SVDD) technique is proposed to improve the non-Gaussian process monitoring performance. The proposed method first concentrates the informative ICs into one subspace for each fault and then the SVDD is employed to examine the variations in all subspaces. Case studies on a simulated process and Tennessee Eastman benchmark process demonstrate the effectiveness of the proposed scheme. The monitoring performances are significantly improved compared with the conventional ICA method.

关键词： independent component analysis multivariate statistics process monitoring support vector data description optimal component selection

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Bagging support vector data description model for batch process monitoring

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JOURNAL OF PROCESS CONTROL 2013年第8期23卷 1090-1096页

作者： Ge, Zhiqiang Song, Zhihuan Zhejiang Univ State Key Lab Ind Control Technol Inst Ind Proc Control Dept Control Sci & Engn Hangzhou 310027 Zhejiang Peoples R China

To improve the monitoring performance of the support vector data description model (SVDD), an ensemble form of SVDD is developed, which is termed as bagging SVDD in this paper. While different kinds of ensemble learning approaches have been developed in the past years, bagging is probably the most traditional and simplest one. By randomly selecting subsets from the original dataset, bagging constructs an individual SVDD model for each of these subsets. For practical utilization, the results of different individual SVDD models are ensembled/combined together. In this paper, two kinds of combination strategies are proposed, named as voting-based strategy and Bayesian-based strategy. Compared to a single SVDD model, the monitoring performance can be improved by the bagging SVDD method in most cases. The feasibility and effectiveness of the proposed method are demonstrated by an industrial semiconductor etch process. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Batch process monitoring support vector data description Ensemble learning Bagging Bayesian combination

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Heuristic sample reduction method for support vector data description

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2016年第1期24卷 298-312页

作者： Sun, Wenzhu Qu, Jianling Chen, Yang Di, Yazhou Gao, Feng Naval Aeronaut Engn Inst Qingdao Branch Qingdao Peoples R China

support vector data description (SVDD) has become one of the most promising methods for one-class classification for finding the boundary of the training set. However, SVDD has a time complexity of O (N-3) and a space complexity of O (N-2). When dealing with very large sizes of training sets, e.g., a training set of the aeroengine gas path parameters with the size of N > 10(6) sampled from several months of flight data, SVDD fails. To solve this problem, a method called heuristic sample reduction (HSR) is proposed for obtaining a reduced training set that is manageable for SVDD. HSR maintains the classification accuracy of SVDD by building the reduced training set heuristically with the samples selected from the original. For demonstration, several artificial datasets and real-world datasets are used in the experiments. In addition, a practical example of the training set of the aeroengine gas path parameters is also used to compare the performance of SVDD based on the proposed HSR with conventional SVDD and other improved methods. The experimental results are very encouraging.

关键词： support vector data description heuristic sample reduction novelty detection one-class classification

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Decentralized plant-wide monitoring based on mutual information-Louvain decomposition and support vector data description diagnosis

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ISA TRANSACTIONS 2023年 133卷 42-52页

作者： Wang, Jing Liu, Pengyang Lu, Shan Zhou, Meng Chen, Xiaolu North China Univ Technol Sch Elect & Control Engn Beijing 100043 Peoples R China Shenzhen Polytech Inst Intelligence Sci & Engn Shenzhen 518055 Peoples R China Peking Univ Sch Coll Engn Beijing 100871 Peoples R China

A decentralized fault detection and diagnosis method is proposed to monitor the nonlinear plant -wide processes effectively. It includes two theme activities: mutual information-Louvain based process decomposition and support vector data descriptions (SVDD) based fault diagnosis. Firstly, the plant -wide process is preliminarily map as an undirected graph corresponding to the mechanism knowledge and process structure. Mutual information (MI) is introduced to depict the correlation degree between different nodes (i.e., process variables), and a Louvain algorithm with MI correlation is proposed to fine decompose the process into reasonable sub-blocks. Then, decentralized SVDD based fault detection method is presented for each sub-block, and the corresponding variable contribution rate is derived. Finally, a Bayesian fusion inference is given to evaluate the detection results of all sub-blocks in an integrated manner. The proposed method is verified in the Tennessee-Eastman (TE) process. (c) 2022 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： Plant-wide process monitoring Mutual information Louvain algorithm support vector data description

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