An efficient weighted Lagrangian twin support vector machine for imbalanced data classification

Shao, Yuan‐Hai; Chen, Wei-Jie; Zhang, Jingjing; Zhen, Wang; Deng, Ning

doi:10.1016/j.patcog.2014.03.008

Cited by 145 publications

(45 citation statements)

References 32 publications

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“…To be consistent with the PCA step used for classification, again the top m = 20 modes of variation were used. We also compared these synthesis methods against subset sampling optimization (SSO) [17], a state-ofthe-art undersampling method, and weighted Lagrangian twin SVM (WLT), a classifier designed to natively deal with class imbalance [14]. No replicated or synthetic instances were used with these methods.…”

Section: Resultsmentioning

confidence: 99%

Prediction of Motor Function in Very Preterm Infants Using Connectome Features and Local Synthetic Instances

Brown

Miller

Booth

et al. 2015

Lecture Notes in Computer Science

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Abstract. We propose a method to identify preterm infants at highest risk of adverse motor function (identified at 18 months of age) using connectome features from a diffusion tensor image (DTI) acquired shortly after birth. For each full-brain DTI, a connectome is constructed and network features are extracted. After further reducing the dimensionality of the feature vector via PCA, SVM is used to discriminate between normal and abnormal motor scores. We further introduce a novel method to produce realistic synthetic training data in order to reduce the effects of class imbalance. Our method is tested on a dataset of 168 DTIs of 115 very preterm infants, scanned between 27 and 45 weeks post-menstrual age. We show that using our synthesized training data can consistently improve classification accuracy while setting a baseline for this challenging prediction problem. This work presents the first image analysis approach to predicting impairment in motor function in preterm-born infants.

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Section: Resultsmentioning

confidence: 99%

Prediction of Motor Function in Very Preterm Infants Using Connectome Features and Local Synthetic Instances

Brown

Miller

Booth

et al. 2015

Lecture Notes in Computer Science

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“…Lagrangian twin support vector machine (LTSVM) constructs a pair of primal problems [19], one primal problem corresponding to one non-parallel plane. It aims to make the same class data samples have the nearest distance to the relevant plane and have adequate distances to the other plane.…”

Section: Improved Twin Support Vector Machinementioning

confidence: 99%

“…This paper chooses a three-layer BP neural network whose middle layer node number is 15, using 200 iterations, and for which the learning rate is 0.01, and the minimum error is 4 × 10 −5 . In reference [19], the parameters are set to c 1 = c 2 = 0.1, and the parameter of LTSVM of is set to β = 0.2, error epsilon = 1 × 10 −5 . It chooses ITWSVM of which parameters are also set to c 1 = c 2 = 0.1, .…”

Section: Simulation and Analysismentioning

confidence: 99%

Leak Location of Pipeline with Multibranch Based on a Cyber-Physical System

Lang

et al. 2017

Information

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Abstract:Data cannot be shared and leakage cannot be located simultaneously among multiple pipeline leak detection systems. Based on cyber-physical system (CPS) architecture, the method for locating leakage for pipelines with multibranch is proposed. The singular point of pressure signals at the ends of pipeline with multibranch is analyzed by wavelet packet analysis, so that the time feature samples could be established. Then, the Fischer-Burmeister function is introduced into the learning process of the twin support vector machine (TWSVM) in order to avoid the matrix inversion calculation, and the samples are input into the improved twin support vector machine (ITWSVM) to distinguish the pipeline leak location. The simulation results show that the proposed method is more effective than the back propagation (BP) neural networks, the radial basis function (RBF) neural networks, and the Lagrange twin support vector machine.

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“…Reports have shown that TSVM is better than both SVM and GEPSVM [55][56][57]. Another advantage of TSVM is that its convergence rate is four times faster than conventional SVM [54].…”

Section: Npsvm Ii-twin Support Vector Machinementioning

confidence: 99%

Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy

Wang

Zhang

Yang

et al. 2015

Entropy

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Aim: To detect pathological brain conditions early is a core procedure for patients so as to have enough time for treatment. Traditional manual detection is either cumbersome, or expensive, or time-consuming. We aim to offer a system that can automatically identify pathological brain images in this paper. Method: We propose a novel image feature, viz., Fractional Fourier Entropy (FRFE), which is based on the combination of Fractional Fourier Transform (FRFT) and Shannon entropy. Afterwards, the Welch's t-test (WTT) and Mahalanobis distance (MD) were harnessed to select distinguishing features. Finally, we introduced an advanced classifier: twin support vector machine (TSVM). Results: A 10ˆK-fold stratified cross validation test showed that this proposed "FRFE + WTT + TSVM" yielded an accuracy of 100.00%, 100.00%, and 99.57% on datasets that contained 66, 160, and 255 brain images, respectively. Conclusions: The proposed "FRFE + WTT + TSVM" method is superior to 20 state-of-the-art methods.

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An efficient weighted Lagrangian twin support vector machine for imbalanced data classification

Cited by 145 publications

References 32 publications

Prediction of Motor Function in Very Preterm Infants Using Connectome Features and Local Synthetic Instances

Prediction of Motor Function in Very Preterm Infants Using Connectome Features and Local Synthetic Instances

Leak Location of Pipeline with Multibranch Based on a Cyber-Physical System

Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy

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