Incremental locally linear fuzzy classifier

Armin Eftekhari, Mojtaba Ahmadieh Khanesar, Mohamad Forouzanfar, Mohammad Teshnehlab

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Optimizing the antecedent part of neuro-fuzzy system is investigated in a number of documents. Current approaches typically suffer from high computational complexity or lack of ability to extract knowledge from a given set of training data. In this paper, we introduce a novel incremental training algorithm for the class of neuro-fuzzy systems that are structured based on local linear classifiers. Linear discriminant analysis is utilized to transform the data into a space in which linear discriminancy of training samples is maximized. The neuro-fuzzy classifier is built in the transformed space, starting from the simplest form. In addition, rule consequent parameters are optimized using a local least square approach.

Original languageEnglish
Title of host publicationApplications of Soft Computing : From Theory to Praxis
Number of pages10
Volume58
PublisherSpringer Verlag
Publication date2009
Pages305-314
ISBN (Print)9783540896180
Publication statusPublished - 2009
Externally publishedYes
SeriesAdvances in Intelligent and Soft Computing
Volume58
ISSN1867-5662

Cite this

Eftekhari, A., Khanesar, M. A., Forouzanfar, M., & Teshnehlab, M. (2009). Incremental locally linear fuzzy classifier. In Applications of Soft Computing: From Theory to Praxis (Vol. 58, pp. 305-314). Springer Verlag. Advances in Intelligent and Soft Computing, Vol.. 58