Interval twin support vector regression algorithm for interval input-output data

Peng, Xinjun; Chen, Dongjing; Kong, Lingbing; Xu, Dong

doi:10.1007/s13042-015-0395-9

Cited by 20 publications

(9 citation statements)

References 26 publications

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“…For example, the height of a man is between 180 cm and 185 cm. Therefore, some researchers have proposed many improved SVR algorithms [14][15][16] which explicitly handle uncertain data and perform better than traditional SVRs. Hao et.al incorporate the concept of fuzzy set theory into the SVM regression model [15].…”

Section: A Subsection Samplementioning

confidence: 99%

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An Incremental Dual nu-Support Vector Regression Algorithm

Zhang

2018

Advances in Knowledge Discovery and Data Mining

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Support vector regression (SVR) has been a hot research topic for several years as it is an effective regression learning algorithm. Early studies on SVR mostly focus on solving large-scale problems. Nowadays, an increasing number of researchers are focusing on incremental SVR algorithms. However, these incremental SVR algorithms cannot handle uncertain data, which are very common in real life because the data in the training example must be precise. Therefore, to handle the incremental regression problem with uncertain data, an incremental dual nu-support vector regression algorithm (dual-v-SVR) is proposed. In the algorithm, a dual-v-SVR formulation is designed to handle the uncertain data at first, then we design two special adjustments to enable the dualv-SVR model to learn incrementally: incremental adjustment and decremental adjustment. Finally, the experiment results demonstrate that the incremental dual-v-SVR algorithm is an efficient incremental algorithm which is not only capable of solving the incremental regression problem with uncertain data, it is also faster than batch or other incremental SVR algorithms.

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Section: A Subsection Samplementioning

confidence: 99%

“…Hao et.al incorporate the concept of fuzzy set theory into the SVM regression model [15]. Peng proposed an interval twin support vector regression algorithm for interval inputoutput data [16]. Several SVR algorithms treat uncertain data as random noise [17][18][19].…”

Section: A Subsection Samplementioning

confidence: 99%

An Incremental Dual nu-Support Vector Regression Algorithm

Zhang

2018

Advances in Knowledge Discovery and Data Mining

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“…Due to the initial structure of the CI-DNN algorithm is constructed by the DVSVR algorithm, so if the DVSVR algorithm is an effective algorithm to solve the interval regression problem, the CI-DNN algorithm will be an efficient algorithm. Based on the reason, we compare the DVSVR algorithm with TSVR algorithm [22] and ITSVR algorithm [23] in the second experiment. These two algorithms are also designed for interval regression problem.…”

Section: B Dvsvr Algorithmmentioning

confidence: 99%

A Dual Neural Network Based On Confidence Intervals For Fuzzy Random Regression Problems

Zhang

et al. 2018

2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Uncertainty in dependent variables or independent variables is typically caused by randomness or fuzziness. But randomness and fuzziness are more and more often appearing simultaneously in independent variables or dependent variables, giving rise to the concept of a fuzzy random variable. Regression analysis is a statistical measure to model the relationship between a dependent variable and one or more independent variables. However, the standard regression algorithms cannot handle the fuzzy random variables, so we propose a dual neural network algorithm based on confidence intervals for fuzzy random regression problems in this paper. The algorithm relies on the expectations of, and variances in, fuzzy random variables to construct the confidence intervals for fuzzy random input-output data. A dual neural network then identifies the sides of the interval output data; one network identifies the upper side, another network identifies the lower side, while a dual v-support vector regression algorithm concurrently constructs the initial structure of the dual neural network. Lastly, a dynamic genetic backpropagation algorithm tunes the parameters of the dual neural network to improve performance. Experiment results demonstrate the validity and applicability of the proposed dual neural network algorithm based on confidence intervals.

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“…[16] noted that when applied to complex nonlinear systems, the performance of SVR is better than kriging technique, radial basis function interpolation and other methods. Recently, ( [17]; [18]; [19]; [20]) incorporated the concept of support vector interval regression into uncertainty analysis. In addition, multioutput SVR ( [21]; [22]; [23]) is also a common uncertainty analysis method.…”

Section: Introductionmentioning

confidence: 99%

Confidence-Constrained Support Vector Regression for Geological Surface Uncertainty Modeling

Chen

2020

IEEE Access

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Reconstruction of complex geological surface is widely used in oil and gas exploration, geological modeling, geological structure analysis, and other fields. It is an important basis for data visualization and visual analysis in these fields. The complexity of geological structures, the inaccuracy and sparsity of seismic interpretation data, and the lack of tectonic morphological information can lead to uncertainty in geological surface reconstruction. The existing geological surface uncertainty characterization and uncertain reconstruction methods have a shortcoming in balancing the interpolation error of high-confidence samples and model structure risk. Based on support vector regression (SVR), a method with confidence constraints for uncertainty characterization and the modeling of geological surfaces is proposed in this paper. The proposed method minimizes the structural risk by adding a regularization term representing the model complexity, integrates high-confidence samples, such as drilling data, based on confidence constraints, and utilizes well path points by assigning appropriate inequality constraints to the corresponding prediction points. The results based on a real-world fault data set show that the uncertainty envelopes and fault realizations generated by the proposed method are constrained by well observations and well paths, effectively reducing the uncertainty.

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Interval twin support vector regression algorithm for interval input-output data

Cited by 20 publications

References 26 publications

An Incremental Dual nu-Support Vector Regression Algorithm

An Incremental Dual nu-Support Vector Regression Algorithm

A Dual Neural Network Based On Confidence Intervals For Fuzzy Random Regression Problems

Confidence-Constrained Support Vector Regression for Geological Surface Uncertainty Modeling

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