Paper Info
Title
Fuzzy robust regression models based on granularity and possibility distribution
Abstract
The characteristic of the fuzzy regression model is to enwrap all the given samples. An interval of fuzzy regression model is created by considering how far a sample is from the central values. That means when samples are widely scattered the size of an interval of the fuzzy model is widened. That is, the fuzziness of the fuzzy regression model is decided by the range of sample distribution. Therefore, many research results on a fuzzy regression model in order to describe the possibility of the target system have been reported. We have proposed two fuzzy robust regression models which remove influences of improper data such as unusual data and outliers. In this paper, we describe the model building of our fuzzy robust regressions by removing influences of improper data.
Year
DOI
Venue
2014
10.1109/SCIS-ISIS.2014.7044751
SCIS&ISIS
Keywords
Field
DocType
fuzzy set theory,possibility theory,regression analysis,sampling methods,statistical distributions,fuzzy robust regression models,granularity distribution,possibility distribution,sample distribution,target system
Sampling distribution,Data mining,Defuzzification,Computer science,Fuzzy logic,Proper linear model,Outlier,Robust regression,Adaptive neuro fuzzy inference system,Fuzzy number
Conference
ISSN
Citations 
PageRank 
2377-6870
0
0.34
References 
Authors
6
2
Name
Order
Citations
PageRank
Yoshiyuki Yabuuchi1387.96
Junzo Watada241184.53