Paper Info
Title
Imprecise Regression And Regression On Fuzzy Data - A Preliminary Discussion
Abstract
The paper provides a discussion of the possibilistic regression method originally proposed by H. Tanaka. This method has the advantage of allowing the learning of an imprecise model, in the form of an interval-valued function. It may lead to an imprecise model even in presence of precise data, which is satisfactory from a learning point of view. Indeed, finding a precise model that perfectly represents the concept to be learned is illusory, due to the existence of the bias caused by the choice of a modeling representation space, the limited amount of data, and the possibility of missing relevant data. However, what is obtained with possibilistic regression is more an imprecise model than a genuine fuzzy one. The paper illustrates and emphasizes this point on environmental data and suggest two different approaches for learning genuine fuzzy regression models from precise data.
Year
DOI
Venue
2006
10.1109/FUZZY.2006.1681908
2006 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5
Keywords
Field
DocType
value function,regression analysis,learning artificial intelligence,fuzzy set theory,neural networks,missing data,data analysis,possibility theory
Data mining,Regression,Computer science,Regression analysis,Fuzzy logic,Possibility theory,Fuzzy set,Artificial intelligence,Environmental data,Missing data,Artificial neural network,Machine learning
Conference
ISSN
Citations 
PageRank 
1098-7584
2
0.42
References 
Authors
3
2
Name
Order
Citations
PageRank
Mathieu Serrurier126726.94
Henri Prade2105491445.02