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Autonomous learning multi-model classifier of 0-Order (ALMMo-0)

Angelov, Plamen, Gu, Xiaowei (2017) Autonomous learning multi-model classifier of 0-Order (ALMMo-0). In: 2017 Evolving and Adaptive Intelligent Systems (EAIS). . pp. 1-7. IEEE ISBN 978-1-5090-6445-8. (doi:10.1109/EAIS.2017.7954832) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90131)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1109/EAIS.2017.7954832

Abstract

In this paper, a new type of 0-order multi-model classifier, called Autonomous Learning Multiple-Model (ALMMo-0), is proposed. The proposed classifier is non-iterative, feedforward and entirely data-driven. It automatically extracts the data clouds from the data per class and forms 0-order AnYa type fuzzy rule-based (FRB) sub-classifier for each class. The classification of new data is done using the “winner takes all” strategy according to the scores of confidence generated objectively based on the mutual distribution and ensemble properties of the data by the sub-classifiers. Numerical examples based on benchmark datasets demonstrate the high performance and computation-efficiency of the proposed classifier.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1109/EAIS.2017.7954832
Uncontrolled keywords: Mathematical model; Training; Computer architecture; Wavelet transforms; Benchmark testing; Feature extraction; Feedforward neural networks; multi-model classifier; autonomous; data-driven; AnYa fuzzy rule-based (FRB) system
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 10 Sep 2021 11:16 UTC
Last Modified: 13 Sep 2021 10:31 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90131 (The current URI for this page, for reference purposes)
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