Structural least square twin support vector machine for classification

Xu, Yitian; Pan, Xianli; Zhou, Zhijian; Yang, Zhiji; Zhang, Yuqun

doi:10.1007/s10489-014-0611-4

Cited by 39 publications

(12 citation statements)

References 22 publications

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“…For Yale experiment datasets, although the classification accuracy of BP-LSSMM is lower than that of SMM 3.75% the maximum and 0.625% the minimum, the running time of BP-LSSMM is faster than that of SMM at least 5.8 times. For ORL experiment datasets, the classification accuracy of BP-LSSMM achieves 100% for pair (5,1) and is lower than that of SMM for pairs (3,8) and (5,4). But the running time of BP-LSSMM is faster than that of SMM at least 1.6 times.…”

Section: Methodsmentioning

confidence: 92%

“…This section recalls some basic concepts and basic results used in the sequel, for details, see [8,[22][23][24].…”

Section: Preliminariesmentioning

confidence: 99%

“…One is solving the Wolfe dual forms, such as support vector machine (SVM) [1,2], twin support vector machine (TSVM) [3], improved TSVM (ITSVM) [4] and so on. Another is solving the primal problems, such as least square SVM (LS-SVM) [5], sparse least square SVM [6], least squares twin multi-class SVM [7], structural least square TSVM [8] and so on [9][10][11]. The use of least squares technology can reduce the computation time (training time and testing time) of classifiers by avoiding quadratic programming problems (QPPs).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Least Squares Support Matrix Machines Based on Bilevel Programming

2016

Int. J. Appl. Math. Mach. Learning

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It is known that the classifications problems for matrix or more higher order tensor data are often met in many real-world applications. If using classical SVM-type methods for such problems, it needs to reshape matrix or tensor data into vectors, which may lead to the destruction of structure information contained in data. In order to overcome the limitation, this paper considers the classification problem with matrices as inputs directly and proposes a novel classification method named as least square support matrix machine (LSSMM). By means of bilevel programming (BP), an iteratively implemented algorithm (BP-LSSMM) for LSSMM is suggested. Experiment results indicate that BP-LSSMM is an effective and competitive classifier for matrix data classification.

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Section: Methodsmentioning

confidence: 92%

“…This section recalls some basic concepts and basic results used in the sequel, for details, see [8,[22][23][24].…”

Section: Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Least Squares Support Matrix Machines Based on Bilevel Programming

2016

Int. J. Appl. Math. Mach. Learning

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“…LSTM based sequential model is proposed in [28] based to EHS records and patients health records. Neural network based models are proposed in [29,30] and SVM based models are explored in [31].…”

Section: Related Workmentioning

confidence: 99%

Robust Cluster-then-label (RCTL) Approach for Heart Disease Prediction

Bodapati¹,

Sajja²

2019

ISI

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Heart attack or stroke occurs due to the narrowed or blocked blood vessels that are present in the heart. If not identified at the early stages of the disease, there is a high probability to loose life. An algorithm that takes the advantage of the inferences given by unsupervised learning is proposed. Our proposed framework leverages the unsupervised information that is hidden in the data to improve the accuracy of the classifier. By using clustering, a label is obtained and this cluster label is included as one of the features. This augmented feature set fed as input to the classifier to predict the disease. For clustering to avoid uncertainty, k-means and spectral clustering are used. If the cluster label predicted by both the methods match, then only that information is included as one of the features. To evaluate the effectiveness of the proposed algorithm two heart disease benchmark datasets, Framingham and stat-log are used. For the classification task Logistic regression, Naïve Baye's and SVM are being used to make the model simple yet effective. Based on the experimental results we claim that by augmenting the feature set with the cluster label using the proposed method significantly improves the performance of the heart disease prediction task.

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“…Combining of RBF kernel F-score feature selection and LS-SVM classifier [28] 83.7 SAS base-Neural networks ensemble [30] 89.01 FDT [31] 77.55 Structural least square twin support vector machine (S-LSTSVM) [32] 87.82…”

Section: B Comparison With Previous Researchmentioning

confidence: 99%

Artificial Neural Network Parameter Tuning Framework For Heart Disease Classification

Yazid¹,

Satria²,

Talib³

et al. 2018

EECSI

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Heart Disease are among the leading cause of death worldwide. The application of artificial neural network as decision support tool for heart disease detection. However, artificial neural network required multitude of parameter setting in order to find the optimum parameter setting that produce the best performance. This paper proposed the parameter tuning framework for artificial neural network. Statlog heart disease dataset and Cleveland heart disease dataset is used to evaluate the performance of the proposed framework. The results show that the proposed framework able to produce high classification accuracy where the overall classification accuracy for Cleveland dataset is 90.9% and 90% for Statlog dataset.

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Structural least square twin support vector machine for classification

Cited by 39 publications

References 22 publications

Least Squares Support Matrix Machines Based on Bilevel Programming

Least Squares Support Matrix Machines Based on Bilevel Programming

Robust Cluster-then-label (RCTL) Approach for Heart Disease Prediction

Artificial Neural Network Parameter Tuning Framework For Heart Disease Classification

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