Autonomous Learning Multi-Model Classifier of 0-Order (ALMMo-0)

Plamen Parvanov Angelov; Xiaowei Gu

doi:10.1109/EAIS.2017.7954832

Autonomous Learning Multi-Model Classifier of 0-Order (ALMMo-0)

Plamen Parvanov Angelov, Xiaowei Gu

Lancaster University

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

33 Citations (Scopus)

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.

Original language	English
Title of host publication	IEEE Conference on Evolving and Adaptive Intelligent Systems 2017
Subtitle of host publication	Conference Proceedings
Editors	Igor Skrjanc, Saso Blazic
Publisher	IEEE Press
ISBN (Print)	9781509064441
DOIs	https://doi.org/10.1109/EAIS.2017.7954832
Publication status	E-pub ahead of print - 22 Jun 2017
Externally published	Yes

Publication series

Name	IEEE Conference on Evolving and Adaptive Intelligent Systems
Volume	2017-May
ISSN (Print)	2330-4863
ISSN (Electronic)	2473-4691

Keywords

AnYa fuzzy rule-based (FRB) system
autonomous
data-driven
multi-model classifier

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10.1109/EAIS.2017.7954832

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Angelov, P. P., & Gu, X. (2017). Autonomous Learning Multi-Model Classifier of 0-Order (ALMMo-0). In I. Skrjanc, & S. Blazic (Eds.), IEEE Conference on Evolving and Adaptive Intelligent Systems 2017: Conference Proceedings Article 7954832 (IEEE Conference on Evolving and Adaptive Intelligent Systems; Vol. 2017-May). IEEE Press. Advance online publication. https://doi.org/10.1109/EAIS.2017.7954832

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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.",

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author = "Angelov, {Plamen Parvanov} and Xiaowei Gu",

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year = "2017",

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doi = "10.1109/EAIS.2017.7954832",

language = "English",

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Autonomous Learning Multi-Model Classifier of 0-Order (ALMMo-0). / Angelov, Plamen Parvanov; Gu, Xiaowei.
IEEE Conference on Evolving and Adaptive Intelligent Systems 2017: Conference Proceedings. ed. / Igor Skrjanc; Saso Blazic. IEEE Press, 2017. 7954832 (IEEE Conference on Evolving and Adaptive Intelligent Systems; Vol. 2017-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference Proceeding (Non-Journal item)

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PY - 2017/6/22

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N2 - 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.

AB - 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.

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PB - IEEE Press

ER -

Autonomous Learning Multi-Model Classifier of 0-Order (ALMMo-0)

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