Home > Research > Publications & Outputs > Autonomously evolving classifier TEDAClass

Electronic data

  • TEDAClass

    Rights statement: This is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, 366, 2016 DOI: 10.1016/j.ins.2016.05.012

    Accepted author manuscript, 248 KB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

View graph of relations

Autonomously evolving classifier TEDAClass

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published

Standard

Autonomously evolving classifier TEDAClass. / Kangin, Dmitry; Angelov, Plamen; Iglesias, José Antonio.
In: Information Sciences, Vol. 366, 20.10.2016, p. 1-11.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

APA

Vancouver

Kangin D, Angelov P, Iglesias JA. Autonomously evolving classifier TEDAClass. Information Sciences. 2016 Oct 20;366:1-11. Epub 2016 May 24. doi: 10.1016/j.ins.2016.05.012

Author

Kangin, Dmitry ; Angelov, Plamen ; Iglesias, José Antonio. / Autonomously evolving classifier TEDAClass. In: Information Sciences. 2016 ; Vol. 366. pp. 1-11.

Bibtex

@article{7e20031ff935464c913612c8d6fd5cfc,
title = "Autonomously evolving classifier TEDAClass",
abstract = "Abstract In this paper we introduce a classifier named TEDAClass (Typicality and Eccentricity based Data Analytics Classifier) which is based on the recently proposed AnYa type fuzzy rule based system. Specifically, the rules of the proposed classifier are defined according to the recently proposed TEDA framework. This novel and efficient systematic methodology for data analysis is a promising addition to the traditional probability as well as to the fuzzy logic. It is centred at non-parametric density estimation derived from the data sample. In addition, the proposed framework is computationally cheap and provides fast and exact per-point processing of the data set/stream. The algorithm is demonstrated to be suitable for different classification tasks. Throughout the paper we give evidence of its applicability to a wide range of practical problems. Furthermore, the algorithm can be easily adapted to different classical data analytics problems, such as clustering, regression, prediction, and outlier detection. Finally, it is very important to remark that the proposed algorithm can work “from scratch” and evolve its structure during the learning process.",
keywords = "Classifiers, Evolving systems, TEDA, Fuzzy systems",
author = "Dmitry Kangin and Plamen Angelov and Iglesias, {Jos{\'e} Antonio}",
note = "This is the author{\textquoteright}s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, 366, 2016 DOI: 10.1016/j.ins.2016.05.012",
year = "2016",
month = oct,
day = "20",
doi = "10.1016/j.ins.2016.05.012",
language = "English",
volume = "366",
pages = "1--11",
journal = "Information Sciences",
issn = "0020-0255",
publisher = "Elsevier Inc.",

}

RIS

TY - JOUR

T1 - Autonomously evolving classifier TEDAClass

AU - Kangin, Dmitry

AU - Angelov, Plamen

AU - Iglesias, José Antonio

N1 - This is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, 366, 2016 DOI: 10.1016/j.ins.2016.05.012

PY - 2016/10/20

Y1 - 2016/10/20

N2 - Abstract In this paper we introduce a classifier named TEDAClass (Typicality and Eccentricity based Data Analytics Classifier) which is based on the recently proposed AnYa type fuzzy rule based system. Specifically, the rules of the proposed classifier are defined according to the recently proposed TEDA framework. This novel and efficient systematic methodology for data analysis is a promising addition to the traditional probability as well as to the fuzzy logic. It is centred at non-parametric density estimation derived from the data sample. In addition, the proposed framework is computationally cheap and provides fast and exact per-point processing of the data set/stream. The algorithm is demonstrated to be suitable for different classification tasks. Throughout the paper we give evidence of its applicability to a wide range of practical problems. Furthermore, the algorithm can be easily adapted to different classical data analytics problems, such as clustering, regression, prediction, and outlier detection. Finally, it is very important to remark that the proposed algorithm can work “from scratch” and evolve its structure during the learning process.

AB - Abstract In this paper we introduce a classifier named TEDAClass (Typicality and Eccentricity based Data Analytics Classifier) which is based on the recently proposed AnYa type fuzzy rule based system. Specifically, the rules of the proposed classifier are defined according to the recently proposed TEDA framework. This novel and efficient systematic methodology for data analysis is a promising addition to the traditional probability as well as to the fuzzy logic. It is centred at non-parametric density estimation derived from the data sample. In addition, the proposed framework is computationally cheap and provides fast and exact per-point processing of the data set/stream. The algorithm is demonstrated to be suitable for different classification tasks. Throughout the paper we give evidence of its applicability to a wide range of practical problems. Furthermore, the algorithm can be easily adapted to different classical data analytics problems, such as clustering, regression, prediction, and outlier detection. Finally, it is very important to remark that the proposed algorithm can work “from scratch” and evolve its structure during the learning process.

KW - Classifiers

KW - Evolving systems

KW - TEDA

KW - Fuzzy systems

U2 - 10.1016/j.ins.2016.05.012

DO - 10.1016/j.ins.2016.05.012

M3 - Journal article

VL - 366

SP - 1

EP - 11

JO - Information Sciences

JF - Information Sciences

SN - 0020-0255

ER -