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Data fusion for fault diagnosis using multi-class support vector machines

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2005年第10期6卷 1030-1039页

作者：胡中辉蔡云泽李远贵许晓鸣 Department of Automation Shanghai Jiao Tong University Shanghai 200030 China

multi-source multi-class classification methods based on multi-class support vector machines and data fusion strategies are proposed in this paper. The centralized and distributed fusion schemes are applied to combine information from several data sources. In the centralized scheme, all information from several data sources is centralized to construct an input space. Then a multi-class support vector Machine classifier is trained. In the distributed schemes, the individual data sources are proc-essed separately and modelled by using the multi-class support vector Machine. Then new data fusion strategies are proposed to combine the information from the individual multi-class support vector Machine models. Our proposed fusion strategies take into account that an support vector Machine (SVM) classifier achieves classification by finding the optimal classification hyperplane with maximal margin. The proposed methods are applied for fault diagnosis of a diesel engine. The experimental results showed that almost all the proposed approaches can largely improve the diagnostic accuracy. The robustness of diagnosis is also improved because of the implementation of data fusion strategies. The proposed methods can also be applied in other fields.

关键词： Data fusion Fault diagnosis multi-class classification multi-class support vector machines Diesel engine

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Intelligent Fault Diagnosis Based on a Hybrid multi-class support vector machines and Case-Based Reasoning Approach

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JOURNAL OF COMPUTATIONAL AND THEORETICAL NANOSCIENCE 2013年第8期10卷 1727-1734页

作者： Lee, Sangwon Seo, Kwang-Kyu Sangmyung Univ Dept Engn Management Cheonan 330790 South Korea Hanyang Univ ERICA Dept Ind & Management Engn Ansan 326791 South Korea

This paper presents a hybrid multi-class support vector machines (SVMs) and case-based reasoning approach for intelligent fault diagnosis. The multi-class classification method usually has a main shortcoming that the binary classifiers used are obtained by training on different binary classification problems, and thus it is unclear whether their real-valued outputs are on comparable scales. In this paper, we try to use additional information, relative outputs of the machines, for final decision. We propose the hybrid approach base on combining multi-class SVMs and case-based reasoning. Case-based reasoning applies with reject option to use the information in order to verify the effectiveness of the proposed approach. The experimental results with real multi-class fault data of rolling bearings show that the proposed approach is useful to improve the fault diagnosis performance.

关键词： Fault Diagnosis multi-class support vector machines Case-Based Reasoning Hybrid Approach

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Fusion of multi-class support vector machines for classification Moving Vehicle Based on multisensor Pavement Strain Data

Fusion of Multi-class Support Vector Machines for Classifica...

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2010 The 3rd International Conference on Power Electronics and Intelligent Transportation System (PEITS 2010)

作者： Zhang Wenbin Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology No.68 Wenchang Road 121 Street,Kunming,China Suo Chunguang School of Sciences Kunming University of Science and Technology No.68 Wenchang Road 121 Street,Kunming,China

In this paper,we propose a special multi-class SVMs(MSVM) data fusion strategy which is applied to classify vehicle based on multiple pavement structural strain time *** centralized and distributed fusion strategies are applied to combine information from several data *** the centralized strategy,all information from several data sources is centralized and combined to construct an input *** a MSVM classifier is *** distributed schemes,the individual data sources are processed separately and modeled by using the *** new data fusion strategies are used to combine the information from the individual MSVM to acquire the final classification *** popular multi-class SVMs algorithms(One-against-all OAA,One-against-one OAO) are used to construct classifier based on aforementioned two fusion strategies,*** results are compared between SVM- based fusion approach and single data source SVM using two MSVM algorithms,*** result shows this SVM- based fusion approach significantly improves the results of classification accuracy and *** proposed multisensor data fusion methods can also be applied in other fields.

关键词： Vehicle classification multisensor data fusion multi-source data classification multi-class support vector machines

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multi-View Scaling support vector machines for classification and Feature Selection

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2020年第7期32卷 1419-1430页

作者： Xu, Jinglin Han, Junwei Nie, Feiping Li, Xuelong Northwestern Polytech Univ Sch Automat Xian 710129 Shaanxi Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Ctr OPT IMagery Anal & Learning OPTIMAL Xian 710072 Shaanxi Peoples R China

With the explosive growth of data, the multi-view data is widely used in many fields, such as data mining, machine learning, computer vision, and so on. Because such data always has a complex structure, i.e., many categories, many perspectives of description and high dimension, how to formulate an accurate and reliable framework for the multi-view classification is a very challenging task. In this paper, we propose a novel multi-view classification method by using multiple multi-class support vector machines (SVMs) with a novel collaborative strategy. Here, each multi-class SVM embeds the scaling factor to renewedly adjust the weight allocation of all features, which is beneficial to highlight more important and discriminative features. Furthermore, we adopt the decision function values to integrate multiple multi-class learners and introduce the confidence score across multiple classes to determine the final classification result. In addition, through a series of the mathematical deduction, we bridge the proposed model with the solvable problem and solve it through an alternating iteration optimization method. We evaluate the proposed method on several image and face datasets, and the experimental results demonstrate that our proposed method performs better than other state-of-the-art learning algorithms.

关键词： support vector machines Feature extraction Collaboration Optimization Training Data mining Machine learning multiple views classification feature selection multi-class support vector machines

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Efficient automated detection of power quality disturbances using nonsubsampled contourlet transform & PCA-SVM

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ENERGY EXPLORATION & EXPLOITATION 2025年

作者： Sinha, Pampa Paul, Kaushik Mohanty, Asit Elzein, Im Mishra, Chandra Sekhar Mahmoud, Mohamed Metwally Mbadjoun Wapet, Daniel Eutyche Al Ayidh, Abdulrahman Althobaiti, Ahmed Hussein, Hany S. Alghamdi, Thamer A. H. Ewais, Ahmed M. KIIT Univ Sch Elect Engn Bhubaneswar India BIT Sindri Dept Elect Engn Dhanbad India Chitkara Univ Ctr Res Impact & Outcome Rajpura India Univ Doha Sci andTechnol Coll Engn & Technol Dept Elect Engn Doha Qatar Odisha Univ Technol Dept Elect Engn Bhubaneswar Orissa India Aswan Univ Fac Energy Engn Dept Elect Engn Aswan 81528 Egypt Univ Yaounde I Natl Adv Sch Engn Yaounde Cameroon King Khalid Univ Coll Engn Elect Engn Dept Abha Saudi Arabia Taif Univ Coll Engn Elect Engn Dept Taif Saudi Arabia Al Baha Univ Fac Engn Elect Engn Dept Al Baha Saudi Arabia Cardiff Univ Wolfson Ctr Magnet Sch Engn Cardiff Wales

The increasing complexity of modern power systems, driven by smart grid technologies, dispersed generation, and renewable energy sources, has made power quality (PQ) evaluation more challenging. Overuse of sensors and large data volumes further complicate accurate PQ assessment. This paper proposes a flexible signal decomposition methodology using the nonsubsampled contourlet transform (NSCT) for accurate PQ event detection. The NSCT's multiscale, multidirectional, and shift-invariant properties enable the decomposition of signals into transient and oscillatory components. High-frequency NSCT subbands are fused to extract oscillatory portions, while low-frequency subbands are averaged to detect transients. Morphological component analysis (MCA) and the split-augmented Lagrangian shrinkage algorithm (SALSA) optimize this process. Principal component analysis (PCA) is applied to the extracted features to reduce dimensionality and improve feature separability. These optimized features are used for training a multi-class support vector machine, with its parameters further optimized for enhanced classification accuracy. The proposed approach demonstrates superior frequency selectivity, adaptability, and computational efficiency, making it suitable for robust PQ disturbance identification and a wide range of signal-processing tasks.

关键词： Signal processing nonsubsampled contourlet wavelet transform multi-class support vector machines power quality disturbances smart grids

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Building behavior scoring model using genetic algorithm and support vector machines

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7th International Conference on Computational Science (ICCS 2007)

作者： Zhang, Defu Chen, Qingshan Wei, Lijun Xiamen Univ Dept Comp Sci Xiamen 361005 Peoples R China Longtop Grp Post doctoral Res Ctr Xiamen 361005 Peoples R China

ISBN: (纸本)9783540725855

In the increasingly competitive credit industry, one of the most interesting and challenging problems is how to manage existing customers. Behavior scoring models have been widely used by financial institutions to forecast customer's future credit performance. In this paper, a hybrid GA+SVM model, which uses genetic algorithm (GA) to search the promising subsets of features and multi-class support vector machines (SVM) to make behavior scoring prediction, is presented. A real life credit data set in a major Chinese commercial bank is selected as the experimental data to compare the classification accuracy rate with other traditional behavior scoring models. The experimental results show that GA+SVM can obtain better performance than other models.

关键词： behavior scoring feature selection genetic algorithm multi-class support vector machines data mining

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Partial Label Learning via GANs With multiclass SVMs and Information Maximization

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第12期32卷 8409-8421页

作者： Fan, Jinfu Wang, Zhongjie Tongji Univ Coll Elect & Informat Engn Dept Control Sci & Engn Shanghai 201804 Peoples R China

Partial label learning (PLL), an important branch of weakly supervised learning, addresses the problem that each instance is associated with a set of candidate labels and only one is correct. In this paper, a novel adversarial model PL-MIGAN is proposed to simultaneously mitigate two fundamental issues of generative adversarial networks (GANs) in PLL: label disambiguation performance of discriminator and instance synthesis quality of generator. First of all, multi-class support vector machines (SVMs) applied in discriminator to disambiguate the candidate labels and identify fake instances. This strategy not only improves the disadvantage that traditional supervised loss is unable to perform disambiguation but also reduces the influence of cumulative error caused by noise label propagation. Furthermore, a partial contrastive loss is constructed to extend the self-supervised contrastive approach to PLL, allowing us to effectively leverage ambiguous labels information. Finally, the generator jointly employ mutual information (MI) and partial contrastive loss to estimate the latent distribution of each class label. In addition, in order to reduce the impact of ambiguous information, an iteratively optimization procedure is designed to update the label confidence matrix as conditional information guides the generation of instance classes. As adversarial learning proceeds, both the discriminator and the generator alternately and iteratively boost their performance. Simulation results reveal the overwhelming performance of PL-MIGAN.

关键词： Phase locked loops Generators Semantics Supervised learning Predictive models Neural networks Training multi-class support vector machines partial label learning mutual information partial contrastive loss

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An expert diagnosis system for classification of human parasite eggs based on multi-class SVM

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EXPERT SYSTEMS WITH APPLICATIONS 2009年第1期36卷 43-48页

作者： Avci, Derya Varol, Asaf Firat Univ Dept Elect & Comp Sci TR-23119 Elazig Turkey

In this paper, it is proposed a new methodology based on invariant moments and multi-class support vector machine (MCSVM) for classification of human parasite eggs in microscopic images. The MCSVM is one of the most used classifiers but it has not used for classification of human parasite eggs to date. This method composes four stages. These are pre-processing stage, feature extraction stage, classification stage, and testing stage. In pre-processing stage, the digital image processing methods, which are noise reduction, contrast enhancement, thresholding, and morphological and logical processes. In feature extraction stage, the invariant moments of pre-processed parasite images are calculated. Finally, in classification stage, the multi-class support vector machine (MCSVM) classifier is used for classification of features extracted feature extraction stage. We used MATLAB software for estimating the success classification rate of proposed approach in this study. For this aim, proposed approach was tested by using test data. At end of test, 97.70% overall success rates were obtained. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： Parasites Parasite eggs Microscopic image multi-class support vector machines Expert systems

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MSVMpack: A multi-class support vector Machine Package

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JOURNAL OF MACHINE LEARNING RESEARCH 2011年 12卷 2293-2296页

作者： Lauer, Fabien Guermeur, Yann LORIA Equipe ABC F-54506 Vandoeuvre Les Nancy France

This paper describes MSVMpack, an open source software package dedicated to our generic model of multi-class support vector machine. All four multi-class support vector machines (M-SVMs) proposed so far in the literature appear as instances of this model. MSVMpack provides for them the first unified implementation and offers a convenient basis to develop other instances. This is also the first parallel implementation for M-SVMs. The package consists in a set of command-line tools with a callable library. The documentation includes a tutorial, a user's guide and a developer's guide.

关键词： multi-class support vector machines open source C

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Sparse least squares support vector training in the reduced empirical feature space

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PATTERN ANALYSIS AND APPLICATIONS 2007年第3期10卷 203-214页

作者： Abe, Shigeo Kobe Univ Grad Sch Sci & Technol Kobe Hyogo 657 Japan

In this paper we discuss sparse least squares support vector machines (sparse LS SVMs) trained in the empirical feature space, which is spanned by the mapped training data. First, we show that the kernel associated with the empirical feature space gives the same value with that of the kernel associated with the feature space if one of the arguments of the kernels is mapped into the empirical feature space by the mapping function associated with the feature space. Using this fact, we show that training and testing of kernel-based methods can be done in the empirical feature space and that training of LS SVMs in the empirical feature space results in solving a set of linear equations. We then derive the sparse LS SVMs restricting the linearly independent training data in the empirical feature space by the Cholesky factorization. support vectors correspond to the selected training data and they do not change even if the value of the margin parameter is changed. Thus for linear kernels, the number of support vectors is the number of input variables at most. By computer experiments we show that we can reduce the number of support vectors without deteriorating the generalization ability.

关键词： Cholesky factorization empirical feature space least squares support vector machines multi-class support vector machines pattern classification RBF kernels support vector machines

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