Dual possibilistic regression analysis using support vector networks

Hao, Pei-Yi

doi:10.1016/j.fss.2019.03.012

Cited by 12 publications

(5 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of the IV-T1FR are comparable to those of Hao's method based on an SVM [46]. If both approaches can fit the fuzzy data effectively, they are not armed to cope with the uncertainty in the IV-T1FS representation.…”

Section: The Iv-t1fr Based On B-splines 2817 Table 2: Comparative Res...mentioning

confidence: 80%

“…NPMs differ from PMs in the sense that the shape of M is not predetermined but can be adjusted to capture unexpected features of the data. For example, K-nearest neighbors, RBF kernel, SVMs, local polynomials, splines, wavelets, etc.,(e.g., [46][58] [62][77] [83]) are regarded as NPMs since the number of parameters grows with the size of the training data. Unlike PMs, NPMs assume that the data cannot be represented by a set of finite parameters, i.e., they assume an infinite dimension of [θ].…”

Section: Nonparametric Modelsmentioning

confidence: 99%

“…Furthermore, all the interval-valued type-1 and type-2 fuzzy regression methods that are published in the literature can be approached with the proposed formalism. For comparison purposes, two benchmark examples are added in Appendix C. The implementation of the IV-T1FR and the IV-T2FR through PMs and NPs are illustrated using a univariate data set given in [46]. The inputs are crisp, and the outputs are symmetrical triangular IV-T1FSs.…”

Section: Illustrative Examplesmentioning

confidence: 99%

See 2 more Smart Citations

Interval-valued fuzzy regression: Philosophical and methodological issues

Boukezzoula

Coquin

2021

Applied Soft Computing

View full text Add to dashboard Cite

Section: The Iv-t1fr Based On B-splines 2817 Table 2: Comparative Res...mentioning

confidence: 80%

Section: Nonparametric Modelsmentioning

confidence: 99%

Section: Illustrative Examplesmentioning

confidence: 99%

See 1 more Smart Citation

Interval-valued fuzzy regression: Philosophical and methodological issues

Boukezzoula

Coquin

2021

Applied Soft Computing

View full text Add to dashboard Cite

“…The first approach, which is called the possibilistic regression, was proposed by Tanaka et al [19] and was developed by many authors (see, e.g. [20,21]). The second approach is based on the distances between the observed values and estimated values of the dependent variable (as in statistical regression), see, e.g.…”

Section: Appendix 2 Fuzzy Regressionmentioning

confidence: 99%

Fuzzy Regression in Predicting Math Achievement, Based on Philosophic-Mindedness, Creativity, Mathematics Self-efficacy, and Mathematics Self-concept

Taheri

Asadi

Shiralipour

2020

Fuzzy Information and Engineering

View full text Add to dashboard Cite

The present study proposes a flexible/soft model for investigating the role of philosophic-mindedness, creativity, mathematics selfefficacy, and mathematics self-concept in predicting math achievement. To do this end, a fuzzy regression model is developed. Two common criteria are used to evaluate the obtained model. Moreover, the predictability of the model is explained. The case study involves 28 male students from Marand, Iran (year 2015-2016) who took part in a test of mathematics achievement. The participants were junior high-school students in science field of study who were asked to answer four questionnaires pertaining to philosophicmindedness, creativity, mathematics self-efficacy, and mathematics self-concept. The analysis of the results through fuzzy regression revealed that philosophical-mindedness is not linked to participants' math achievement, while the variables of creativity, mathematics self-efficacy, and mathematics self-concept are positively correlated. The results of this study provide suggestions to any educational system in planning for students' math achievement growth. The proposed methodology is general, so that it can be employed in other educational levels and fields of study.

show abstract

“…It optimized two smaller-sized QPPs respectively too—one of which formulated high value of the interval output data and another calculated low value of them. In Hao 50 the upper model and the lower model were estimated by solving two smaller SVM-type QPPs, which have the same strategy as Twin-SVM and ITSVR and reduced computational time complexity of training regression model successfully.…”

Section: Introductionmentioning

confidence: 99%

Dual possibilistic regression models of support vector machines and application in power load forecasting

Yang

2020

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

Power load forecasting is an important guarantee of safe, stable, and economic operation of power systems. It is appropriate to use interval data to represent fuzzy information in power load forecasting. The dual possibilistic regression models approximate the observed interval data from the outside and inside directions, respectively, which can estimate the inherent uncertainty existing in the given fuzzy phenomenon well. In this article, efficient dual possibilistic regression models of support vector machines based on solving a group of quadratic programming problems are proposed. And each quadratic programming problem containing fewer optimization variables makes the training speed of the proposed approach fast. Compared with other interval regression approaches based on support vector machines, such as quadratic loss support vector machine approach and two smaller quadratic programming problem support vector machine approach, the proposed approach is more efficient on several artificial datasets and power load dataset.

show abstract

Dual possibilistic regression analysis using support vector networks

Cited by 12 publications

References 38 publications

Interval-valued fuzzy regression: Philosophical and methodological issues

Interval-valued fuzzy regression: Philosophical and methodological issues

Fuzzy Regression in Predicting Math Achievement, Based on Philosophic-Mindedness, Creativity, Mathematics Self-efficacy, and Mathematics Self-concept

Dual possibilistic regression models of support vector machines and application in power load forecasting

Contact Info

Product

Resources

About