Atlantis Press Paper Details: All-Pairs Evolving Fuzzy Classifiers for On-line Multi-Class Classification Problems

In this paper, we propose a novel design of evolving fuzzy classifiers in case of multi-class classification problems. Therefore, we exploit the concept of all-pairs aka all-versus-all classification using binary classifiers for each pair of classes, which has some advantages over direct multi-class as well as one-versus-rest classification variants. Regressionbased as well as singleton class label fuzzy classifiers are used as architectures for the binary classifiers, which are evolved and incrementally trained based on the concepts included in the FLEXFIS family (a connection of eVQ and recursive fuzzily weighted least squares). The classification phase considers the preference levels of each pair of classes stored in a preference relation matrix and uses a weighted voting scheme of preference levels, including reliability aspects. The advantage of the new evolving fuzzy classifier concept over single model (using direct multi-class classification concept) and multi model (using one-versus-rest classification concept) architectures will be underlined by empirical evaluations and comparisons at the end of the paper based on high-dimensional real-world multi-class classification problems.

		back to table of contents

title:		All-Pairs Evolving Fuzzy Classifiers for On-line Multi-Class Classification Problems
publication:		EUSFLAT
part of series:		Advances in Intelligent Systems Research
pages:		372 - 379
DOI:		To be assigned soon (how to use a DOI)
author(s):		Edwin Lughofer
publication date:		July 2011
keywords:		multi-class problems, all-pairs classification, evolving fuzzy classifiers, preference relation matrix, reliability
abstract:		In this paper, we propose a novel design of evolving fuzzy classifiers in case of multi-class classification problems. Therefore, we exploit the concept of all-pairs aka all-versus-all classification using binary classifiers for each pair of classes, which has some advantages over direct multi-class as well as one-versus-rest classification variants. Regressionbased as well as singleton class label fuzzy classifiers are used as architectures for the binary classifiers, which are evolved and incrementally trained based on the concepts included in the FLEXFIS family (a connection of eVQ and recursive fuzzily weighted least squares). The classification phase considers the preference levels of each pair of classes stored in a preference relation matrix and uses a weighted voting scheme of preference levels, including reliability aspects. The advantage of the new evolving fuzzy classifier concept over single model (using direct multi-class classification concept) and multi model (using one-versus-rest classification concept) architectures will be underlined by empirical evaluations and comparisons at the end of the paper based on high-dimensional real-world multi-class classification problems.
copyright:		© Atlantis Press. This article is distributed under the terms of the Creative Commons Attribution License, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited.
full text:		eusflatlfa2011_S8_1.pdf (1851 K)