Maximum margin of twin spheres machine with pinball loss for imbalanced data classification

Xu, Yitian; Wang, Qian; Pang, Xinying; Tian, Ying

doi:10.1007/s10489-017-0961-9

Cited by 28 publications

(16 citation statements)

References 44 publications

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“…According to the analysis in Figure 2, with the amount of data increases, the prediction accuracy curve of the proposed algorithm always shows an upward trend, and the accuracy curves of other literature algorithms fluctuate up and down, especially the algorithm in Literature [20] algorithm, The prediction accuracy curve of the algorithm first rises and then decreases, and the fluctuation range is large. The accuracy of Literature [24] is relatively high.…”

Section: ) Anti-attack Capability Of the Stream Processmentioning

confidence: 96%

“…Finally, this new attribute weighting method is incorporated into Naive Bayes for unbalanced data classification, but the method has weaker attack resistance. Literature [20] focused on analyzing an unbalanced data classification method, and experimentally verified the performance of the method, and obtained good results, but the method has a higher data packet loss rate; Literature [21] discussed the use of GPUs to speed up gravity calculations, and used data classification methods in the process. Classify the data of the gravity calculation process and assign them to the GPU threads in parallel to calculate the gravity at the same time.…”

Section: Related Workmentioning

confidence: 99%

“…Effective anti-attack efficiency Using the calculation method of formula (17), the network load prediction accuracy of the proposed algorithm and Literature [20], Literature [21], Literature [22], Literature [23], and Literature [24] are calculated, and the comparison results are shown in Figure 2.…”

Section: ) Anti-attack Capability Of the Stream Processmentioning

confidence: 99%

See 2 more Smart Citations

Stream of Unbalanced Medical Big Data Using Convolutional Neural Network

2020

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In order to address the problem that the traditional algorithm can not predict the network link load effectively, which leads to high packet loss and energy loss, long turnaround time, slow stream rate and poor anti-attack ability, the paper proposes the stream algorithm of unbalanced medical big data based on convolutional neural network (CNN). The proposed algorithm included two stages:In the first stage, the decomposition-prediction model was constructed, the combined wavelet analysis and neural network analysis were used to complete the network link load prediction; In the second stage, based on the network link load situation, we analyzed the structure of each layer of convolution neural network, constructed the medical big data stream optimization model, introduced the ReLu function to calculate the convolution neural network, solved the optimization model, and completed the stream processing of unbalanced medical big data. The experimental results show that the network link load prediction accuracy of the proposed stream algorithm is as high as 93%, the lowest packet loss rate is only 2.0%, the energy loss of the stream process is low, the rate is fast, and the anti-attack efficiency is high, which is more conducive to the realization of data stream. INDEX TERMS Convolutional neural network, unbalanced medical big data, network load forecasting, ReLu function, data stream.

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Section: ) Anti-attack Capability Of the Stream Processmentioning

confidence: 96%

Section: Related Workmentioning

confidence: 99%

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Stream of Unbalanced Medical Big Data Using Convolutional Neural Network

2020

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“…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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“…Due to its high learning speed, TSVM has attracted interest in recent years. Many improvements have also been proposed [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], where the twin hypersphere SVM (THSVM) [25][26][27][28] is an excellent improvement of TSVM. Unlike TSVM, THSVM seeks a pair of hyperspheres, instead of two nonparallel hyperplanes, to depict two classes of samples.…”

Section: Introductionmentioning

confidence: 99%

An Effective Multiclass Twin Hypersphere Support Vector Machine and Its Practical Engineering Applications

Zhang

Wang

et al. 2019

Electronics

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Twin-KSVC (Twin Support Vector Classification for K class) is a novel and efficient multiclass twin support vector machine. However, Twin-KSVC has the following disadvantages. (1) Each pair of binary sub-classifiers has to calculate inverse matrices. (2) For nonlinear problems, a pair of additional primal problems needs to be constructed in each pair of binary sub-classifiers. For these disadvantages, a new multi-class twin hypersphere support vector machine, named Twin Hypersphere-KSVC, is proposed in this paper. Twin Hypersphere-KSVC also evaluates each sample into 1-vs-1-vs-rest structure, as in Twin-KSVC. However, our Twin Hypersphere-KSVC does not seek two nonparallel hyperplanes in each pair of binary sub-classifiers as in Twin-KSVC, but a pair of hyperspheres. Compared with Twin-KSVC, Twin Hypersphere-KSVC avoids computing inverse matrices, and for nonlinear problems, can apply the kernel trick to linear case directly. A large number of comparisons of Twin Hypersphere-KSVC with Twin-KSVC on a set of benchmark datasets from the UCI repository and several real engineering applications, show that the proposed algorithm has higher training speed and better generalization performance.

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Maximum margin of twin spheres machine with pinball loss for imbalanced data classification

Cited by 28 publications

References 44 publications

Stream of Unbalanced Medical Big Data Using Convolutional Neural Network

Stream of Unbalanced Medical Big Data Using Convolutional Neural Network

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

An Effective Multiclass Twin Hypersphere Support Vector Machine and Its Practical Engineering Applications

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