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Regression analysis based on fuzzy evidence theory.
| Content Provider | CiteSeerX |
|---|---|
| Author | Petit-Renaud, Simon Denoeux, Thierry |
| Abstract | We propose a new approach to functional regression based on the fuzzy evidence theory. This method uses a training set for computing a fuzzy belief structure that quantifies different sort of uncertainties, such as nonspecificity, discord in the output data, or low density around the input data. The method can use a very large class of output data, such as real, interval or fuzzy numbers, or, more generally, what we called fuzzy belief structure numbers. We show that our approach can be regarded as a kind of a fuzzy system and we present the analogies with the fuzzy model proposed by Yager in [13], which can take output discord into account. The proposed model can provide predictions, in a variety of forms depending on the accuracy of the available information, such as: a crisp output, a fuzzy output, a probability distribution and some information criteria (nonspecificity, strife, ignorance degree). 1. Introduction Although statistical regression analysis is one of the most widely us... |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Fuzzy Evidence Theory Regression Analysis Output Data Input Data Probability Distribution Fuzzy Belief Structure Number Large Class Fuzzy Belief Structure Information Criterion Functional Regression Different Sort Ignorance Degree Fuzzy Output Output Discord Fuzzy System Available Information Fuzzy Model Statistical Regression Analysis Crisp Output Training Set New Approach Fuzzy Number Low Density |
| Content Type | Text |
