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

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

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Standard

Autonomous Learning Multi-Model Classifier of 0-Order (ALMMo-0). / Angelov, Plamen Parvanov; Gu, Xiaowei.
IEEE Conference on Evolving and Adaptive Intelligent Systems 2017. 2017. p. 1-7.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Angelov, PP & Gu, X 2017, Autonomous Learning Multi-Model Classifier of 0-Order (ALMMo-0). in IEEE Conference on Evolving and Adaptive Intelligent Systems 2017. pp. 1-7, IEEE Conference on Evolving and Adaptive Intelligent Systems , 31/05/17.

APA

Angelov, P. P., & Gu, X. (2017). Autonomous Learning Multi-Model Classifier of 0-Order (ALMMo-0). In IEEE Conference on Evolving and Adaptive Intelligent Systems 2017 (pp. 1-7)

Vancouver

Angelov PP, Gu X. Autonomous Learning Multi-Model Classifier of 0-Order (ALMMo-0). In IEEE Conference on Evolving and Adaptive Intelligent Systems 2017. 2017. p. 1-7

Author

Angelov, Plamen Parvanov ; Gu, Xiaowei. / Autonomous Learning Multi-Model Classifier of 0-Order (ALMMo-0). IEEE Conference on Evolving and Adaptive Intelligent Systems 2017. 2017. pp. 1-7

Bibtex

@inproceedings{6521995c291c4252a17f2fa0f79107f9,
title = "Autonomous Learning Multi-Model Classifier of 0-Order (ALMMo-0)",
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.",
author = "Angelov, {Plamen Parvanov} and Xiaowei Gu",
year = "2017",
month = may,
day = "31",
language = "English",
pages = "1--7",
booktitle = "IEEE Conference on Evolving and Adaptive Intelligent Systems 2017",
note = "IEEE Conference on Evolving and Adaptive Intelligent Systems ; Conference date: 31-05-2017 Through 02-06-2017",

}

RIS

TY - GEN

T1 - Autonomous Learning Multi-Model Classifier of 0-Order (ALMMo-0)

AU - Angelov, Plamen Parvanov

AU - Gu, Xiaowei

PY - 2017/5/31

Y1 - 2017/5/31

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.

M3 - Conference contribution/Paper

SP - 1

EP - 7

BT - IEEE Conference on Evolving and Adaptive Intelligent Systems 2017

T2 - IEEE Conference on Evolving and Adaptive Intelligent Systems

Y2 - 31 May 2017 through 2 June 2017

ER -