Evolving Fuzzy Rule-based Classifiers from Data Streams

Angelov, Plamen and Zhou, Xiaowei (2008) Evolving Fuzzy Rule-based Classifiers from Data Streams. IEEE Transactions on Fuzzy Systems, 16 (6). pp. 1462-1475. ISSN 1063-6706

This is the latest version of this item.

Full text not available from this repository.


A new approach to the online classification of streaming data is introduced in this paper. It is based on a self-developing (evolving) fuzzy-rule-based (FRB) classifier system of Takagi-Sugeno ( eTS) type. The proposed approach, called eClass (evolving class ifier), includes different architectures and online learning methods. The family of alternative architectures includes: 1) eClass0, with the classifier consequents representing class label and 2) the newly proposed method for regression over the features using a first-order eTS fuzzy classifier, eClass1. An important property of eClass is that it can start learning ldquofrom scratch.rdquo Not only do the fuzzy rules not need to be prespecified, but neither do the number of classes for eClass (the number may grow, with new class labels being added by the online learning process). In the event that an initial FRB exists, eClass can evolve/develop it further based on the newly arrived data. The proposed approach addresses the practical problems of the classification of streaming data (video, speech, sensory data generated from robotic, advanced industrial applications, financial and retail chain transactions, intruder detection, etc.). It has been successfully tested on a number of benchmark problems as well as on data from an intrusion detection data stream to produce a comparison with the established approaches. The results demonstrate that a flexible (with evolving structure) FRB classifier can be generated online from streaming data achieving high classification rates and using limited computational resources.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Fuzzy Systems
Additional Information:
"©2008 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."
Uncontrolled Keywords:
ID Code:
Deposited By:
Deposited On:
30 Oct 2008 09:55
Last Modified:
08 Dec 2022 00:26

Available Versions of this Item