Health Evaluation of MVB Based on SVDD and Sample Reduction

Li, Zhaozhao; Wang, Lide; Yang, Yueyi; Du, Xuhua; Song, Hui

doi:10.1109/access.2019.2904600

Cited by 11 publications

(11 citation statements)

References 33 publications

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“…ones obtained on the full data set. This has spurred the design of sampling methods that try to identify supportvector candidates in the original data, to retain them in the sample [9][10][11][12][14][15][16][17]. A common approach is to select socalled "boundary points" as support-vector candidates, e.g., observations that are dissimilar to each other [9,17].…”

Section: Sample Boundarymentioning

confidence: 99%

“…Another useful categorization is to separate observations into boundary points and inner points. There are different ways to define a boundary of X [9][10][11][12][14][15][16][17]. For this article, we define boundary points as observations with density values close to the minimum empirical density.…”

Section: Definition 1 (Level Set)mentioning

confidence: 99%

“…Reduction Sampling [9][10][11][12][13][14][15][16][17][18] Kernel Matrix [24][25][26][27][28][29] Optimization [5][6][7][8] Fast Inference [2,[30][31][32][33] post-processing pre-processing efficient if the number of support vectors is low. Also note that under mild assumptions, SVDD is equivalent to ν-SVM [20].…”

Section: Fast Trainingmentioning

confidence: 99%

“…Even efficient solvers like decomposition methods [5][6][7][8] result in training times prohibitive for many applications. In these cases, sampling for data reduction is essential [9][10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

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Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary

Englhardt¹,

Trittenbach²,

Kottke³

et al. 2020

Preprint

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Support Vector Data Description (SVDD) is a popular one-class classifiers for anomaly and novelty detection. But despite its effectiveness, SVDD does not scale well with data size. To avoid prohibitive training times, sampling methods select small subsets of the training data on which SVDD trains a decision boundary hopefully equivalent to the one obtained on the full data set. According to the literature, a good sample should therefore contain so-called boundary observations that SVDD would select as support vectors on the full data set. However, non-boundary observations also are essential to not fragment contiguous inlier regions and avoid poor classification accuracy. Other aspects, such as selecting a sufficiently representative sample, are important as well. But existing sampling methods largely overlook them, resulting in poor classification accuracy.In this article, we study how to select a sample considering these points. Our approach is to frame SVDD sampling as an optimization problem, where constraints guarantee that sampling indeed approximates the original decision boundary. We then propose RAPID, an efficient algorithm to solve this optimization problem. RAPID does not require any tuning of parameters, is easy to implement and scales well to large data sets. We evaluate our approach on real-world and synthetic data. Our evaluation is the most comprehensive one for SVDD sampling so far. Our results show that RAPID outperforms its competitors in classification accuracy, in sample size, and in runtime.

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Section: Sample Boundarymentioning

confidence: 99%

Section: Definition 1 (Level Set)mentioning

confidence: 99%

Section: Fast Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary

Englhardt¹,

Trittenbach²,

Kottke³

et al. 2020

Preprint

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show abstract

“…Huan et al [18] developed a model selection-based support vector data description (SVDD) to detect the outlier in sensor data, the model selection strategy can help select a relatively optimal decision model. Li et al [19] proposed an anomaly detection approach based on the SVDD and densitybased spatial clustering of application with noise (DBSCAN) for detecting the anomalies of the multifunction vehicle bus (MVB).…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection for Controller Area Network in Braking Control System With Dynamic Ensemble Selection

Yang

Wang

et al. 2019

IEEE Access

Self Cite

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The controller area networks (CAN) in the braking control system of metro trains are used to transmit the important control instruction and condition information, whose anomaly will endanger the security of trains running seriously. Due to the harsh work environment, there are various known and previously unknown fault types, current scheduled maintenance cannot detect early anomaly in time, and constructing an accurate and stable anomaly detector is a challenging task. In this paper, an anomaly detection approach is proposed to detect anomaly based on a dynamic ensemble selection system (DESS) without the expert knowledge, which involves two-class and one-class classifiers, and the base classifiers are trained with the network features extracted from the physical-layer information. To conduct the fusion, the support function of ''distance-based'' classifier is redefined as a class-conditional probability density function, and the source competence of base classifier is estimated by the entropy-based method in validate space and extended to entire decision space using the normalized Gaussian potential function. For different fault types, the competence classifiers are selected and the anomaly detection result is finally achieved by weighted majority voting. The comparative experiments are included in this paper to demonstrate the effectiveness and robustness in anomaly detection, including varying fault types.

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Fault diagnosis of the train communication network based on weighted support vector machine

Wang

Yang

2020

IEEJ Transactions Elec Engng

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Multifunction vehicle bus (MVB) is the most widely used train communication network which transmits controlling and supervising data. The faults of MVB will heavily affect the train's safe and stable operation. Due to the harsh operating environment and distributed structure, the MVB fault diagnosis has always been a difficult issue in the maintenance of the train. Many MVB faults will distort the physical waveforms and cause serious packet loss. Thus, we have extracted waveform features to characterize different MVB faults and turned the fault diagnosis into a pattern recognition problem. Then a classifier based on weighted support vector machine (WSVM) has been trained to diagnose and locate network faults. Considering that samples locating in different positions of the feature space have different influences on the support vector machine (SVM) hyperplane, we have proposed a multi‐hop edge approaching method to assign sample weights in WSVM. To identify the position of the tested sample, the hops from its location to the classification margin have been counted. The less the hop‐count, the closer to the classification margin and the larger the sample weight. Compared with SVM and other WSVM methods, the proposed method has better performance on the artificial synthetic datasets, the MVB dataset, and the benchmark datasets. © 2020 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

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Health Evaluation of MVB Based on SVDD and Sample Reduction

Cited by 11 publications

References 33 publications

Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary

Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary

Anomaly Detection for Controller Area Network in Braking Control System With Dynamic Ensemble Selection

Fault diagnosis of the train communication network based on weighted support vector machine

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