Variable precision rough set model
Journal of Computer and System Sciences
The nature of statistical learning theory
The nature of statistical learning theory
Rough mereological foundations for design, analysis, synthesis, and control in distributed systems
Information Sciences: an International Journal - From rough sets to soft computing
Predicting Time Series with Support Vector Machines
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
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IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
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Fuzzy Sets and Systems - Theme: Learning and modeling
Support vector interval regression networks for interval regression analysis
Fuzzy Sets and Systems - Theme: Learning and modeling
A note on fuzzy regression model with fuzzy input and output data for manpower forecasting
Fuzzy Sets and Systems - Theme: Learning and modeling
Extended fuzzy regression models using regularization method
Information Sciences—Informatics and Computer Science: An International Journal
Rough set based 1-v-1 and 1-v-r approaches to support vector machine multi-classification
Information Sciences: an International Journal
Extended support vector interval regression networks for interval input-output data
Information Sciences: an International Journal
Parameterized rough set model using rough membership and Bayesian confirmation measures
International Journal of Approximate Reasoning
A Support Vector Approach to Censored Targets
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Handbook of Granular Computing
Handbook of Granular Computing
Brighthouse: an analytic data warehouse for ad-hoc queries
Proceedings of the VLDB Endowment
Rough Sets in Data Warehousing
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
An evolutionary approach for achieving scalability with general regression neural networks
Natural Computing: an international journal
Dual models for possibilistic regression analysis
Computational Statistics & Data Analysis
Support vector interval regression machine for crisp input and output data
Fuzzy Sets and Systems
Constructive and algebraic methods of the theory of rough sets
Information Sciences: an International Journal
Rapid and brief communication: Rough support vector clustering
Pattern Recognition
Petri net models for the semi-automatic construction of large scale biological networks
Natural Computing: an international journal
Interval regression analysis by quadratic programming approach
IEEE Transactions on Fuzzy Systems
Interval regression analysis using quadratic loss support vector machine
IEEE Transactions on Fuzzy Systems
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Support vector regression provides an alternative to the neural networks in modeling non-linear real-world patterns. Rough values, with a lower and upper bound, are needed whenever the variables under consideration cannot be represented by a single value. This paper describes two approaches for the modeling of rough values with support vector regression (SVR). One approach, by attempting to ensure that the predicted high value is not greater than the upper bound and that the predicted low value is not less than the lower bound, is conservative in nature. On the contrary, we also propose an aggressive approach seeking a predicted high which is not less than the upper bound and a predicted low which is not greater than the lower bound. The proposal is shown to use @e-insensitivity to provide a more flexible version of lower and upper possibilistic regression models. The usefulness of our work is realized by modeling the rough pattern of a stock market index, and can be taken advantage of by conservative and aggressive traders.