Paper Info
Title
A hybrid method based on -transform for robust estimators.
Abstract
Robust regression analysis is a stable method to fit a given dataset especially when the data set includes outliers. It has been developed with various approaches due to its practical usefulness and insensitivity to outliers. In this paper, we propose a new hybrid algorithm based on several robust methods combined with F (fuzzy)-transform to compare its performances with several robust methods. Some M-estimators such as L1, L1−L2 and Fair have been used as existing robust methods. L2, which is very sensitive to outliers, also has been used for comparison. Additionally, the Orthogonal distance regression (ODR) is introduced to enhance the deficiency of basic distance of error. To find the estimated parameters which minimize the objective functions, the Genetic Algorithm (GA) is used as an optimization algorithm. The performances are measured in RMSE,MAD and MAPE to compare their accuracies. Three examples are provided and the results show that the proposed hybrid methods are much more superior to existing robust methods such as L1, L1−L2 and Fair.
Year
DOI
Venue
2019
10.1016/j.ijar.2018.10.003
International Journal of Approximate Reasoning
Keywords
Field
DocType
Robust estimators,M-estimators,F-transform,Orthogonal distance regression (ODR),Optimization algorithm,Genetic Algorithm (GA)
Hybrid algorithm,Regression,Fuzzy logic,Outlier,Algorithm,Robust regression,Optimization algorithm,Artificial intelligence,Machine learning,Genetic algorithm,Mathematics,Estimator
Journal
Volume
Issue
ISSN
104
1
0888-613X
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Jin Hee Yoon17710.77
DeokHwan Kyeong200.68
Kisung Seo314118.95