Boundary detection and sample reduction for one-class Support Vector Machines

Zhu, Fu-Chen; Ye, Ning; Yu, Wei; Xu, Sheng; Li, Guobao

doi:10.1016/j.neucom.2013.07.002

Cited by 59 publications

(23 citation statements)

References 20 publications

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“…For , , i =4, 5, …, 9, 4366, 4367, …, 4371, all nearest neighbors whose size up to 11 belong to one class and the neighbors from different class are shown when the nearest neighbor size is over 11. This results in 0 for k=1, 3,5,7,9,11, and 0 for k=13, 15. We classify , based on its number of nonzero entropies, , in Figure 3.…”

Section: Graphical Analysis Of Nearest Neighbors Entropymentioning

confidence: 99%

Instance-based entropy fuzzy support vector machine for imbalanced data

Cho

Lee

Chang

2019

Pattern Anal Applic

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Imbalanced classification has been a major challenge for machine learning because many standard classifiers mainly focus on balanced datasets and tend to have biased results towards the majority class. We modify entropy fuzzy support vector machine (EFSVM) and introduce instance-based entropy fuzzy support vector machine (IEFSVM). Both EFSVM and IEFSVM use the entropy information of k-nearest neighbors to determine the fuzzy membership value for each sample which prioritizes the importance of each sample. IEFSVM considers the diversity of entropy patterns for each sample when increasing the size of neighbors, k, while EFSVM uses single entropy information of the fixed size of neighbors for all samples. By varying k, we can reflect the component change of sample's neighbors from near to far distance in the determination of fuzzy value membership. Numerical experiments on 35 public and 12 real-world imbalanced datasets are performed to validate IEFSVM and area under the receiver operating characteristic curve (AUC) is used to compare its performance with other SVMs and machine learning methods. IEFSVM shows a much higher AUC value for datasets with high imbalance ratio, implying that IEFSVM is effective in dealing with the class imbalance problem.

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Section: Graphical Analysis Of Nearest Neighbors Entropymentioning

confidence: 99%

Instance-based entropy fuzzy support vector machine for imbalanced data

Cho

Lee

Chang

2019

Pattern Anal Applic

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“…A good example of this can be seen during training one-class methods based on Support Vector Machines [48]. Here one is interested only in objects that have high potential of becoming future support vectors [59]. Thus, reducing the number of potential candidates will speed-up the training procedure.…”

Section: The Role Of Instance Reduction In One-class Classificationmentioning

confidence: 99%

Instance reduction for one-class classification

et al. 2018

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Instance reduction techniques are data preprocessing methods originally developed to enhance the nearest neighbor rule for standard classification. They reduce the training data by selecting or generating representative examples of a given problem. These algorithms have been designed and widely analyzed in multi-class problems providing very competitive results. However, this issue was rarely addressed in the context of one-class classification. In

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“…SVM has been widely used in industrial fault diagnosis . In the training phase of SVM, samples in different locations make different effects on the hyperplane . Samples located in the edge region are most likely to be support vectors that determine the position of the classification hyperplane.…”

Section: Introductionmentioning

confidence: 99%

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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Boundary detection and sample reduction for one-class Support Vector Machines

Cited by 59 publications

References 20 publications

Instance-based entropy fuzzy support vector machine for imbalanced data

Instance-based entropy fuzzy support vector machine for imbalanced data

Instance reduction for one-class classification

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

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