A ν-twin support vector machine based regression with automatic accuracy control

Rastogi, Reshma; Anand, Pritam; Chandra, Suresh

doi:10.1007/s10489-016-0860-5

Cited by 22 publications

(9 citation statements)

References 12 publications

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“…For modelling, we used five different machine learning techniques-logistic regression (LR) [43,51], naive Bayes classifier (NB) [44], support vector machine (SVM) [45][46][47], random forest (RF) [48,54], and deep neural network (DNN) [49] to compare the results. The brief explanation of five machine learning techniques is provided in Appendix A.…”

Section: Modelling and Evaluationmentioning

confidence: 99%

Predicting Patent Transactions Using Patent-Based Machine Learning Techniques

Kim

Geum

2020

IEEE Access

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Technology transfer becomes imperative in recent business environment where technology changes rapidly and its complexity becomes sophisticated. Among various context of technology transfer, it is especially important to predict patent transactions in such fast-changing industries. Therefore, this study aims to suggest a predictive model for patent transaction considering a wide range of decision variables. For this purpose, this study highlighted two considerations-technological impact of technology donor and technological proximity in previous patent transactions. Six factors are employed for developing our predictive model-technological strength, knowledge accumulation, technological protection scope, technological jurisdiction, technological strength of companies, and previous history of patent transfers. Five machine learning techniques are employed. As a result, we find that technological strength of companies and previous transfer history significantly affect technology transfer. This study is expected to be used in practice where the technology buying decision is very complicated and comprehensive.

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Section: Modelling and Evaluationmentioning

confidence: 99%

Predicting Patent Transactions Using Patent-Based Machine Learning Techniques

Kim

Geum

2020

IEEE Access

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“…It is easy to see that the RBF kernel achieves the optimal results. Then, the average results of the proposed methods and other four existing methods (TSVR [15], V-TSVR [17], KNNWTSVR [19] and Asy V-TSVR [20]) with 10 independent runs are shown in Table 5, where Type A, B, C and D denote four different types of noises (41)-(44). Obviously, the results of Table 5 show that the proposed method achieves the optimal result of SSE.…”

Section: Performance Test Of Wtwtsvrmentioning

confidence: 99%

“…In 2010, Peng [15] proposed a twin support vector regression (TSVR), which can be used to establish the prediction model for industrial data. After that, some improved TSVR methods [16][17][18][19][20][21][22] were proposed. By introducing a K-nearest neighbor (KNN) weighted matrix into the optimization problem in TSVR, the modified algorithms [16,19] were proposed to improve the performance of TSVR.…”

Section: Introductionmentioning

confidence: 99%

“…By introducing a K-nearest neighbor (KNN) weighted matrix into the optimization problem in TSVR, the modified algorithms [16,19] were proposed to improve the performance of TSVR. A V-TSVR [17] and asymmetric V-TSVR [20] were proposed to enhance the generalization ability by tuning new model parameters. To solve the ill-conditioned problem in the dual objective functions of the traditional TSVR, an implicit Lagrangian formulation for TSVR [18] was proposed to ensure that the matrices in the formulation are always positive semidefinite matrices.…”

Section: Introductionmentioning

confidence: 99%

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End-Point Static Control of Basic Oxygen Furnace (BOF) Steelmaking Based on Wavelet Transform Weighted Twin Support Vector Regression

Gao

Shen

et al. 2019

Complexity

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A static control model is proposed based on wavelet transform weighted twin support vector regression (WTWTSVR). Firstly, new weighted matrix and coefficient vector are added into the objective functions of twin support vector regression (TSVR) to improve the performance of the algorithm. The performance test confirms the effectiveness of WTWTSVR. Secondly, the static control model is established based on WTWTSVR and 220 samples in real plant, which consists of prediction models, control models, regulating units, controller, and BOF. Finally, the results of proposed prediction models show that the prediction error bound with 0.005% in carbon content and 10°C in temperature can achieve a hit rate of 92% and 96%, respectively. In addition, the double hit rate of 90% is the best result by comparing with four existing methods. The results of the proposed static control model indicate that the control error bound with 800 Nm3 in the oxygen blowing volume and 5.5 tons in the weight of auxiliary materials can achieve a hit rate of 90% and 88%, respectively. Therefore, the proposed model can provide a significant reference for real BOF applications, and also it can be extended to the prediction and control of other industry applications.

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“…To overcome these difficulties, Shao et al [ 39 ] proposed another twin regression model, called

-TSVR, which considers the principle of structural risk minimization. Later, Rastogi et al [ 40 ] extended

-TSVR and proposed

-TSVR, which can automatically optimize parameters

1 and

2 based on sample data. By using the pinball loss function, Xu et al [ 41 ] further developed an asymmetric

-twin support vector regression, called Asy-

-TSVR, which can effectively reduce noise interference and improve the generalization performance.…”

Section: Introductionmentioning

confidence: 99%

Twin Least Square Support Vector Regression Model Based on Gauss-Laplace Mixed Noise Feature with Its Application in Wind Speed Prediction

Zhang

Liu

Wang

et al. 2020

Entropy

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In this article, it was observed that the noise in some real-world applications, such as wind power forecasting and direction of the arrival estimation problem, does not satisfy the single noise distribution, including Gaussian distribution and Laplace distribution, but the mixed distribution. Therefore, combining the twin hyperplanes with the fast speed of Least Squares Support Vector Regression (LS-SVR), and then introducing the Gauss–Laplace mixed noise feature, a new regressor, called Gauss-Laplace Twin Least Squares Support Vector Regression (GL-TLSSVR), for the complex noise. Subsequently, we apply the augmented Lagrangian multiplier method to solve the proposed model. Finally, we apply the short-term wind speed data-set to the proposed model. The results of this experiment confirm the effectiveness of our proposed model.

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A ν-twin support vector machine based regression with automatic accuracy control

Cited by 22 publications

References 12 publications

Predicting Patent Transactions Using Patent-Based Machine Learning Techniques

Predicting Patent Transactions Using Patent-Based Machine Learning Techniques

End-Point Static Control of Basic Oxygen Furnace (BOF) Steelmaking Based on Wavelet Transform Weighted Twin Support Vector Regression

Twin Least Square Support Vector Regression Model Based on Gauss-Laplace Mixed Noise Feature with Its Application in Wind Speed Prediction

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