Linear regression analysis for fuzzy/crisp input and fuzzy/crisp output data
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Possibilistic data analysis and its similarity to rough sets
Data mining, rough sets and granular computing
The Journal of Machine Learning Research
Analyzing the nonlinear time series of turbulent flows with kernel interval regression machine
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Inferring operating rules for reservoir operations using fuzzy regression and ANFIS
Fuzzy Sets and Systems
Support vector machines for interval discriminant analysis
Neurocomputing
Asymmetrical interval regression using extended ε -SVM with robust algorithm
Fuzzy Sets and Systems
Quadratic-interval Bass model for new product sales diffusion
Expert Systems with Applications: An International Journal
Interval regression analysis using support vector networks
Fuzzy Sets and Systems
Interval Regression Analysis with Soft-Margin Reduced Support Vector Machine
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Dual models for possibilistic regression analysis
Computational Statistics & Data Analysis
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Support vector interval regression machine for crisp input and output data
Fuzzy Sets and Systems
Fuzzy regression models using the least-squares method based on the concept of distance
IEEE Transactions on Fuzzy Systems
Building confidence-interval-based fuzzy random regression models
IEEE Transactions on Fuzzy Systems
Dual interval model and its application to decision making
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Fuzzy ridge regression with non symmetric membership functions and quadratic models
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Dual mathematical models based on rough approximations in data analysis
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
A revisited approach to linear fuzzy regression using trapezoidal fuzzy intervals
Information Sciences: an International Journal
Conservative and aggressive rough SVR modeling
Theoretical Computer Science
A Midpoint--Radius approach to regression with interval data
International Journal of Approximate Reasoning
Interval regression by tolerance analysis approach
Fuzzy Sets and Systems
Fuzzy regression with quadratic programming: an application to financial data
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
A reduced support vector machine approach for interval regression analysis
Information Sciences: an International Journal
A novel nonlinear programming approach for estimating CAPM beta of an asset using fuzzy regression
Expert Systems with Applications: An International Journal
Applying a Fuzzy and Neural Approach for Forecasting the Foreign Exchange Rate
International Journal of Fuzzy System Applications
Semidefinite Programming-Based Method for Implementing Linear Fitting to Interval-Valued Data
International Journal of Fuzzy System Applications
Hi-index | 0.01 |
When we use linear programming in possibilistic regression analysis, some coefficients tend to become crisp because of the characteristic of linear programming. On the other hand, a quadratic programming approach gives more diverse spread coefficients than a linear programming one. Therefore, to overcome the crisp characteristic of linear programming, we propose interval regression analysis based on a quadratic programming approach. Another advantage of adopting a quadratic programming approach is to be able to integrate both the property of central tendency in least squares and the possibilistic property in fuzzy regression. By changing the weights of the quadratic function, we can analyze the given data from different viewpoints. For data with crisp inputs and interval outputs, the possibility and necessity models can be considered. Therefore, the unified quadratic programming approach obtaining the possibility and necessity regression models simultaneously is proposed. Even though there always exist possibility estimation models, the existence of necessity estimation models is not guaranteed if we fail to assume a proper function fitting to the given data as a regression model. Thus, we consider polynomials as regression models since any curve can be represented by the polynomial approximation. Using polynomials, we discuss how to obtain approximation models which fit well to the given data where the measure of fitness is newly defined to gauge the similarity between the possibility and the necessity models. Furthermore, from the obtained possibility and necessity regression models, a trapezoidal fuzzy output can be constructed