A review on multi-class TWSVM

Ding, Shifei; Zhao, Xingyu; Zhang, Jian; Zhang, Xiekai; Xue, Yu

doi:10.1007/s10462-017-9586-y

Cited by 44 publications

(25 citation statements)

References 72 publications

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“…The runtime of our approach is mainly determined be the training of the required number of base classifiers which is quadratic in the overall number classes. It therefore brings into range ordinal classifier cascades based on more sophisticated but also more complex base classifiers [14,15,25,38,46,59]. To our knowledge, our screening is the first one that applies memoization techniques to ordinal classification.…”

Section: Discussionmentioning

confidence: 99%

Detecting Ordinal Subcascades

Lausser¹,

Schäfer²,

Kühlwein³

et al. 2020

Neural Process Lett

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Ordinal classifier cascades are constrained by a hypothesised order of the semantic class labels of a dataset. This order determines the overall structure of the decision regions in feature space. Assuming the correct order on these class labels will allow a high generalisation performance, while an incorrect one will lead to diminished results. In this way ordinal classifier systems can facilitate explorative data analysis allowing to screen for potential candidate orders of the class labels. Previously, we have shown that screening is possible for total orders of all class labels. However, as datasets might comprise samples of ordinal as well as non-ordinal classes, the assumption of a total ordering might be not appropriate. An analysis of subsets of classes is required to detect such hidden ordinal substructures. In this work, we devise a novel screening procedure for exhaustive evaluations of all order permutations of all subsets of classes by bounding the number of enumerations we have to examine. Experiments with multi-class data from diverse applications revealed ordinal substructures that generate new and support known relations.

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Section: Discussionmentioning

confidence: 99%

Detecting Ordinal Subcascades

Lausser¹,

Schäfer²,

Kühlwein³

et al. 2020

Neural Process Lett

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“…After solving dual problems (27) and (30) by the standard QPP solver, we can obtain the solutions to primal problems (12) and (13) by Proposition 1 according to KKT conditions without proof. Proposition 1.…”

Section: Theorem 1 Optimization Problemsmentioning

confidence: 99%

“…The above nonparallel models were mainly proposed for binary classification problems. However, most real-world applications [29][30][31][32] are related to multiclass classifications such as disease diagnosis, fault detection, image recognition, and text categorization. Therefore, many researchers are interested in extending SVM models from binary to multiclass classification.…”

Section: Introductionmentioning

confidence: 99%

A Multiclass Nonparallel Parametric-Margin Support Vector Machine

Zhang

Chen

et al. 2021

Information

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The twin parametric-margin support vector machine (TPMSVM) is an excellent kernel-based nonparallel classifier. However, TPMSVM was originally designed for binary classification, which is unsuitable for real-world multiclass applications. Therefore, this paper extends TPMSVM for multiclass classification and proposes a novel K multiclass nonparallel parametric-margin support vector machine (MNP-KSVC). Specifically, our MNP-KSVC enjoys the following characteristics. (1) Under the “one-versus-one-versus-rest” multiclass framework, MNP-KSVC encodes the complicated multiclass learning task into a series of subproblems with the ternary output {−1,0,+1}. In contrast to the “one-versus-one” or “one-versus-rest” strategy, each subproblem not only focuses on separating the two selected class instances but also considers the side information of the remaining class instances. (2) MNP-KSVC aims to find a pair of nonparallel parametric-margin hyperplanes for each subproblem. As a result, these hyperplanes are closer to their corresponding class and at least one distance away from the other class. At the same time, they attempt to bound the remaining class instances into an insensitive region. (3) MNP-KSVC utilizes a hybrid classification and regression loss joined with the regularization to formulate its optimization model. Then, the optimal solutions are derived from the corresponding dual problems. Finally, we conduct numerical experiments to compare the proposed method with four state-of-the-art multiclass models: Multi-SVM, MBSVM, MTPMSVM, and Twin-KSVC. Experimental results demonstrate the feasibility and effectiveness of MNP-KSVC in terms of multiclass accuracy and learning time.

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“…In this method, the negative dataset is more representative than the original samples. The multiclass SVM model has been shown to be more robust than simple random sampling [37].…”

Section: The Multiclass Support Vector Machine (Svm)mentioning

confidence: 99%

Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy

Zhu

et al. 2019

Energies

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Condition monitoring is used to assess the reliability and equipment efficiency of wind turbines. Feature extraction is an essential preprocessing step to achieve a high level of performance in condition monitoring. However, the fluctuating conditions of wind turbines usually cause sudden variations in the monitored features, which may lead to an inaccurate prediction and maintenance schedule. In this scenario, this article proposed a novel methodology to detect the multiple levels of faults of rolling bearings in variable operating conditions. First, signal decomposition was carried out by variational mode decomposition (VMD). Second, the statistical features were calculated and extracted in the time domain. Meanwhile, a permutation entropy analysis was conducted to estimate the complexity of the vibrational signal in the time series. Next, feature selection techniques were applied to achieve improved identification accuracy and reduce the computational burden. Finally, the ranked feature vectors were fed into machine learning algorithms for the classification of the bearing defect status. In particular, the proposed method was performed over a wide range of working regions to simulate the operational conditions of wind turbines. Comprehensive experimental investigations were employed to evaluate the performance and effectiveness of the proposed method.

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A review on multi-class TWSVM

Cited by 44 publications

References 72 publications

Detecting Ordinal Subcascades

Detecting Ordinal Subcascades

A Multiclass Nonparallel Parametric-Margin Support Vector Machine

Condition Monitoring for the Roller Bearings of Wind Turbines under Variable Working Conditions Based on the Fisher Score and Permutation Entropy

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