A maximum margin and minimum volume hyper-spheres machine with pinball loss for imbalanced data classification

Xu, Yitian; Yang, Zhiji; Zhang, Yuqun; Pan, Xianli; Wang, Lai-Sheng

doi:10.1016/j.knosys.2015.12.005

Cited by 54 publications

(13 citation statements)

References 31 publications

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“…Here we only cope with binary classification problems, so one class is labeled as the minority while the rest merge as the majority in multiclass cases which is similar to other researchers' preprocess [25, 39, 40]. Table 2 shows the detailed information about the dataset including sample capacity, the number of attributes, the numbers of the minority samples and majority samples, and the imbalance ratio.…”

Section: Resultsmentioning

confidence: 99%

A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM

Wang

Luo

Huang

et al. 2017

Computational Intelligence and Neuroscience

113

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Class imbalance ubiquitously exists in real life, which has attracted much interest from various domains. Direct learning from imbalanced dataset may pose unsatisfying results overfocusing on the accuracy of identification and deriving a suboptimal model. Various methodologies have been developed in tackling this problem including sampling, cost-sensitive, and other hybrid ones. However, the samples near the decision boundary which contain more discriminative information should be valued and the skew of the boundary would be corrected by constructing synthetic samples. Inspired by the truth and sense of geometry, we designed a new synthetic minority oversampling technique to incorporate the borderline information. What is more, ensemble model always tends to capture more complicated and robust decision boundary in practice. Taking these factors into considerations, a novel ensemble method, called Bagging of Extrapolation Borderline-SMOTE SVM (BEBS), has been proposed in dealing with imbalanced data learning (IDL) problems. Experiments on open access datasets showed significant superior performance using our model and a persuasive and intuitive explanation behind the method was illustrated. As far as we know, this is the first model combining ensemble of SVMs with borderline information for solving such condition.

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Section: Resultsmentioning

confidence: 99%

A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM

Wang

Luo

Huang

et al. 2017

Computational Intelligence and Neuroscience

113

View full text Add to dashboard Cite

show abstract

“…According to formula (13) and formula (14), we can get the approximate solution of formula (17) about v as:…”

Section: Celmmentioning

confidence: 99%

Aeroengines Remaining Useful Life Prediction Based on Improved C-Loss ELM

Zhang

Bai

et al. 2020

IEEE Access

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A correntropy induced loss (C-loss) function is a loss function developed based on entropy theory. Due to the non-convexity of the C-loss function, ELM based on C-loss has been proven to have better regression effects and robustness. Inspired by generalized quantile learning, we find that there is a further improvement in the C-loss function. The main work of this paper is to propose a new improved C-loss function, which lead to a novel algorithm ICELM, and the half-optimized algorithm is used to solve the ICELM. Experiments on several benchmark data sets prove the superiority of the ICELM. Compared with CELM, ICELM has more efficient regression performance and robustness, and is obviously more suitable for complex aeroengines remaining useful life prediction field which affected by noise and outliers. Experiments on a benchmark C-MAPSS dataset also validate the theory.

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“…The idea of SVDD or hypersphere SVM classification algorithm [14]- [19] is to map all feature vectors into a highdimensional space, in which a minimum radius hypersphere satisfying some constraints is established, the hypersphere contains almost all the homogeneous sample points. The original space surface corresponding to the hypersphere or concentric hypersphere is regarded as the classification decision surface.…”

Section: Related Workmentioning

confidence: 99%

SVDTWDD Method for High Correct Recognition Rate Classifier With Appropriate Rejection Recognition Regions

Yang

Teng

et al. 2020

IEEE Access

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At present, regions of the same class determined by Support Vector Machines (SVM) classifier, Support Vector Domain Description (SVDD) classifier and Deep Learning (DL) classifier may occupy regions of other classes or unknown classes in feature space. There exists a risk that samples of other classes or unknown classes are wrongly classified as a known class. In this paper, the Support Vector Domain Tightly Wrapping Description Design (SVDTWDD) method with appropriate rejection regions and the corresponding incremental learning algorithm are proposed to overcome the above problem. The main work includes: (1) We develop a construction algorithm of the tightly wrapping set for the homogeneous feature set; (2) Based on the homogeneous feature set and tightly wrapping set, a novel algorithm is presented for obtaining the tightly wrapping surface of the homogeneous feature region; (3) The method for constructing all the public regions outside of the tightly wrapping surface and the intersections of wrapping regions in two different tightly wrapping surfaces, as the rejection region of the classifier; (4) An incremental algorithm is also presented based on the SVD-TWDD method. The experimental results with UCI data sets show that the correct recognition rate of our proposed method is nearly100% even if with a low rejection rate.

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A maximum margin and minimum volume hyper-spheres machine with pinball loss for imbalanced data classification

Cited by 54 publications

References 31 publications

A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM

A Novel Ensemble Method for Imbalanced Data Learning: Bagging of Extrapolation-SMOTE SVM

Aeroengines Remaining Useful Life Prediction Based on Improved C-Loss ELM

SVDTWDD Method for High Correct Recognition Rate Classifier With Appropriate Rejection Recognition Regions

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