A new approach to dealing with missing values in data-driven fuzzy modeling

R.J. Almeida, U. Kaymak, J.M. Costa Sousa, da

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

5 Citations (Scopus)
119 Downloads (Pure)

Abstract

Real word data sets often contain many missing elements. Most algorithms that automatically develop a rule-based model are not well suited to deal with incomplete data. The usual technique is to disregard the missing values or substitute them by a best guess estimate, which can bias the results. In this paper we propose a new method for estimating the parameters of a Takagi-Sugeno fuzzy model in the presence of incomplete data. We also propose an inference mechanism that can deal with the incomplete data. The presented method has the added advantage that it does not require imputation or iterative guess-estimate of the missing values. This methodology is applied to fuzzy modeling of a classification and regression problem. The performance of the obtained models are comparable with the results obtained when using a complete data set.
Original languageEnglish
Title of host publicationProceedings of the World Congress on Computational Intelligence (WCCI 2010), July 18-23, 2010, Barcelona, Spain
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages312-318
ISBN (Print)978-1-4244-6919-2
DOIs
Publication statusPublished - 2010
Eventconference; World Congress on Computational Intelligence (WCCI 2010); 2010-07-18; 2010-07-23 -
Duration: 18 Jul 201023 Jul 2010

Conference

Conferenceconference; World Congress on Computational Intelligence (WCCI 2010); 2010-07-18; 2010-07-23
Period18/07/1023/07/10
OtherWorld Congress on Computational Intelligence (WCCI 2010)

Fingerprint

Dive into the research topics of 'A new approach to dealing with missing values in data-driven fuzzy modeling'. Together they form a unique fingerprint.

Cite this