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Evolving fuzzy rule-based classifiers

Angelov, Plamen and Zhou, Xiaowei and Klawonn, F (2007) Evolving fuzzy rule-based classifiers. In: Symposium, 2007-04-012007-04-04, Honolulu, Hawaii, USA.

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Abstract

In this paper a new method for training single-model and multi-model fuzzy classifiers incrementally and adaptively is proposed, which is called FLEXFIS-Class. The evolving scheme for the single-model case exploits a conventional zero-order fuzzy classification model architecture with Gaussian fuzzy sets in the rules antecedents, crisp class labels in the rule consequents and rule weights standing for confidence values in the class labels. In the multi-model case FLEXFIS-Class exploits the idea of regression by an indicator matrix to evolve a Takagi-Sugeno fuzzy model for each separate class and combines the single models' predictions to a final classification statement. The paper includes a technique for increasing the prediction quality, whenever a drift in a data stream occurs. An empirical analysis will be given based on an online, adaptive image classification framework, where images showing production items should be classified into good or bad ones. This analysis will include the comparison of evolving single- and multi-model fuzzy classifiers with conventional batch modelling approaches with respect to achieved prediction accuracy on new online data. It will also be shown that multi-model architecture can outperform conventional single-model architecture (`classical' fuzzy classification models) for all data sets with respect to prediction accuracy. (c) IEEE Press

Item Type: Conference or Workshop Item (Paper)
Journal or Publication Title: Symposium
Additional Information: "©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE." "This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder."
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 933
Deposited By: Dr. Plamen Angelov
Deposited On: 11 Jan 2008 13:42
Refereed?: Yes
Published?: Published
Last Modified: 21 Mar 2014 10:25
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/933

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