A Discussion of Attribute Reduction in Fuzzy Rough Sets Using Support Vector Machine

2006 ◽  
Author(s):  
Eric. C.C. Tsang ◽  
Degang Chen ◽  
Suyun Zhao ◽  
Qiang He
Keyword(s):  
Support Vector Machine ◽  
Rough Sets ◽  
Attribute Reduction ◽  
Support Vector ◽  
Fuzzy Rough Sets

Photovoltaic Power Generation Forecasting Model with Improved Support Vector Machine Regression Based on Rough Set and Similar Day

2013 ◽  
Vol 805-806 ◽  
pp. 114-120 ◽  
Author(s):  
Hua Wei Mei ◽  
Juan Juan Ma
Keyword(s):  
Support Vector Machine ◽  
Rough Set ◽  
Historical Data ◽  
Attribute Reduction ◽  
Support Vector ◽  
Forecasting Model ◽  
Parametric Search ◽  
Similar Day ◽  
Regression Theory ◽  
Improved Support Vector Machine

To diminish the effect of photovoltaic (PV) randomization on the power system, combining attribute reduction of rough set with support vector machine (SVM) regression theory, this paper applies SVM regression to directly forecast the output of the PV array, and is based on setting rough set as front-end processor and attribute reduction of historical data. According to the type of forecasting day, this paper selects multiple reasonable similar days (SD) from historical data and uses RS-SVR model to make predication. After repeated accuracy verification, the text used radial basis function as kernel function, and use parametric search and cross-validation method to determine the parameters. Finally, this paper compared average relative error of the RS-SVR forecasting model and SVR forecasting model, and verified that the RS-SVR forecasting model can effectively solve the problem of PV power output forecasting and obtain satisfactory results.

Unsupervised attribute reduction for mixed data based on fuzzy rough sets

2021 ◽  
Author(s):  
Zhong Yuan ◽  
Hongmei Chen ◽  
Tianrui Li ◽  
Zeng Yu ◽  
Binbin Sang ◽  
...  
Keyword(s):  
Rough Sets ◽  
Attribute Reduction ◽  
Mixed Data ◽  
Fuzzy Rough Sets

scholarly journals Rough Support Vector Machine for Classification with Interval and Incomplete Data

2020 ◽  
Vol 10 (1) ◽  
pp. 47-56 ◽  
Author(s):  
Robert K. Nowicki ◽  
Konrad Grzanek ◽  
Yoichi Hayashi
Keyword(s):  
Support Vector Machine ◽  
Rough Sets ◽  
Incomplete Data ◽  
Input Data ◽  
Missing Values ◽  
Classification Scheme ◽  
Equivalence Classes ◽  
Support Vector ◽  
Positive Boundary

AbstractThe paper presents the idea of connecting the concepts of the Vapnik’s support vector machine with Pawlak’s rough sets in one classification scheme. The hybrid system will be applied to classifying data in the form of intervals and with missing values [1]. Both situations will be treated as a cause of dividing input space into equivalence classes. Then, the SVM procedure will lead to a classification of input data into rough sets of the desired classes, i.e. to their positive, boundary or negative regions. Such a form of answer is also called a three–way decision. The proposed solution will be tested using several popular benchmarks.

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