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Building Fuzzy Robust Regression Model Based on Granularity and Possibility Distribution

Chapter
Part of the Studies in Big Data book series (SBD, volume 8)

Abstract

The characteristic of the fuzzy regression model is to enwrap all the given samples. The fuzzy regression model enables us to take the possibility interval for a granular instead of a single numerical value. This granular provides the wider treatment for us to human-centered understanding of the latent system. Such a granule or 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 a granular or 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, outliers make the fuzzy regression model distorted. This chapter describes the model building of fuzzy robust regression from the perspective of granularity by removing improper data based on genetic algorithm. Moreover, let us build the fuzzy regression model that places the largest grade on the central point of scattering samples.

Keywords

Fuzzy regression model Robustness Granularity Possibility distribution Human centered understanding Outliers Genetic algorithm 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of EconomicsShimonoseki City UniversityShimonosekiJapan
  2. 2.Graduate School of Information, Production and SystemsWaseda UniversityKitakyushuJapan

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