Incorporating monotonic domain knowledge in support vector learning for data mining regression problems

Chuang, Hui Chi; Chen, Chih Chuan; Li, Sheng-Tun

doi:10.1007/s00521-019-04661-4

Cited by 8 publications

(6 citation statements)

References 41 publications

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“…As non-monotone regressors firstorder TS fuzzy system with cosine (NonCOS) and Gaussian membership functions (NonGAUSS), non-monotone multilayer perceptron (NonMLP), and non-monotone support vector regression (NonSVR) are considered. Monotone first-order TS fuzzy system with Gaussian membership functions (MonGAUSS, [26]), monotone multilayer perceptron (MonMLP, [51]), monotone MIN-MAX network (MonMM, [15]) and monotone support vector regression (MonSVR, [6], does not guarantee monotonicity) were taken into account as monotone regression models.…”

Section: Large Datasetsmentioning

confidence: 99%

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Monotonic Fuzzy Systems With Goniometric Membership Functions

Hušek

2024

Int. J. Fuzzy Syst.

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Fuzzy logic-based systems are nowadays commonly used in nonlinear function approximation when incoming data are available. Their main advantage is that the resulting rules can be interpreted understandably. Nevertheless, when the data are noisy an overfitting may occur which leads to poor accuracy and generalization ability. Prior information about the nonlinear function may improve fuzzy system performance. In this paper the case when the function is monotonic with respect to some or all variables is considered. Sufficient conditions for the monotonicity of first-order Takagi–Sugeno fuzzy systems with raised cosine membership functions are derived. Performance of the proposed fuzzy system is tested on two benchmark datasets

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Section: Large Datasetsmentioning

confidence: 99%

“…Possibilities of how to include the requirement of monotonicity have been considered for most machine learning-based techniques. Monotonic support vector regression (SVR) and support vector machines (SVM) were investigated in [6,7], respectively. Pelckmans et al [8] used the duality to include monotonicity conditions in kernel regression.…”

Section: Introductionmentioning

confidence: 99%

Monotonic Fuzzy Systems With Goniometric Membership Functions

Hušek

2024

Int. J. Fuzzy Syst.

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“…In contrast to that, in some works, such as [13,17,[26][27][28][29], monotonicity knowledge was incorporated already in training. In these articles, the monotonicity requirements were added as constraints-either hard [17,26,28,29] or soft [13,26]-to the data-based optimization of the model parameters.…”

Section: Introductionmentioning

confidence: 99%

Compensating Data Shortages in Manufacturing with Monotonicity Knowledge

et al. 2021

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Systematic decision making in engineering requires appropriate models. In this article, we introduce a regression method for enhancing the predictive power of a model by exploiting expert knowledge in the form of shape constraints, or more specifically, monotonicity constraints. Incorporating such information is particularly useful when the available datasets are small or do not cover the entire input space, as is often the case in manufacturing applications. We set up the regression subject to the considered monotonicity constraints as a semi-infinite optimization problem, and propose an adaptive solution algorithm. The method is applicable in multiple dimensions and can be extended to more general shape constraints. It was tested and validated on two real-world manufacturing processes, namely, laser glass bending and press hardening of sheet metal. It was found that the resulting models both complied well with the expert’s monotonicity knowledge and predicted the training data accurately. The suggested approach led to lower root-mean-squared errors than comparative methods from the literature for the sparse datasets considered in this work.

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“…In contrast to that, in some works like [13,17,[26][27][28][29]] monotonicity knowledge is incorporated already in training. In these papers, the monotonicity requirements are added as constraints -either hard [17,26,28,29] or soft [13,26] -to the data-based optimization of the model parameters.…”

Section: Introductionmentioning

confidence: 99%

“…In [28] and [13], probabilistic monotonicity notions are used. In [26][27][28][29] support vector regressors in the linear-programming or the more standard quadraticprogramming form, Gaussian process regressors, and neural network models are considered, respectively, and monotonicity of these models is enforced by constraints on the model derivatives at predefined sampling points [26,28,29] or on the model increments between predefined pairs of sampling points [27].…”

Section: Introductionmentioning

confidence: 99%

Compensating data shortages in manufacturing with monotonicity knowledge

Kurnatowski¹,

Schmid²,

Link³

et al. 2020

Preprint

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We present a regression method for enhancing the predictive power of a model by exploiting expert knowledge in the form of shape constraints, or more specifically, monotonicity constraints. Incorporating such information is particularly useful when the available data sets are small or do not cover the entire input space, as is often the case in manufacturing applications. We set up the regression subject to the considered monotonicity constraints as a semi-infinite optimization problem, and we propose an adaptive solution algorithm. The method is conceptually simple, applicable in multiple dimensions and can be extended to more general shape constraints. It is tested and validated on two real-world manufacturing processes, namely laser glass bending and press hardening of sheet metal. It is found that the resulting models both comply well with the expert's monotonicity knowledge and predict the training data accurately. The suggested approach leads to lower root-mean-squared errors than comparative methods from the literature for the sparse data sets considered in this work.

show abstract

Incorporating monotonic domain knowledge in support vector learning for data mining regression problems

Cited by 8 publications

References 41 publications

Monotonic Fuzzy Systems With Goniometric Membership Functions

Monotonic Fuzzy Systems With Goniometric Membership Functions

Compensating Data Shortages in Manufacturing with Monotonicity Knowledge

Compensating data shortages in manufacturing with monotonicity knowledge

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