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Unsupervised classification of data streams based on typicality and eccentricity data analytics

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

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Unsupervised classification of data streams based on typicality and eccentricity data analytics. / Costa, Bruno Sielly Jales; Bezerra, Clauber Gomes; Guedes, Luiz Affonso; Angelov, Plamen Parvanov.

2016 IEEE International Conference on Fuzzy Systems (FUZZ). Vancouver Canada : IEEE, 2016. p. 58-63.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Costa, BSJ, Bezerra, CG, Guedes, LA & Angelov, PP 2016, Unsupervised classification of data streams based on typicality and eccentricity data analytics. in 2016 IEEE International Conference on Fuzzy Systems (FUZZ). IEEE, Vancouver Canada, pp. 58-63. https://doi.org/10.1109/FUZZ-IEEE.2016.7737668

APA

Costa, B. S. J., Bezerra, C. G., Guedes, L. A., & Angelov, P. P. (2016). Unsupervised classification of data streams based on typicality and eccentricity data analytics. In 2016 IEEE International Conference on Fuzzy Systems (FUZZ) (pp. 58-63). IEEE. https://doi.org/10.1109/FUZZ-IEEE.2016.7737668

Vancouver

Costa BSJ, Bezerra CG, Guedes LA, Angelov PP. Unsupervised classification of data streams based on typicality and eccentricity data analytics. In 2016 IEEE International Conference on Fuzzy Systems (FUZZ). Vancouver Canada: IEEE. 2016. p. 58-63 https://doi.org/10.1109/FUZZ-IEEE.2016.7737668

Author

Costa, Bruno Sielly Jales ; Bezerra, Clauber Gomes ; Guedes, Luiz Affonso ; Angelov, Plamen Parvanov. / Unsupervised classification of data streams based on typicality and eccentricity data analytics. 2016 IEEE International Conference on Fuzzy Systems (FUZZ). Vancouver Canada : IEEE, 2016. pp. 58-63

Bibtex

@inproceedings{9fcc034af7c441e0a1336b6f019b89dc,
title = "Unsupervised classification of data streams based on typicality and eccentricity data analytics",
abstract = "In this paper, we propose a novel approach to unsupervised and online data classification. The algorithm is based on the statistical analysis of selected features and development of a self-evolving fuzzy-rule-basis. It starts learning from an empty rule basis and, instead of offline training, it learns “on-the-fly”.It is free of parameters and, thus, fuzzy rules, number, size or radius of the classes do not need to be pre-defined. It is very suitable for the classification of online data streams with realtimeconstraints. The past data do not need to be stored in memory, since that the algorithm is recursive, which makes it memory and computational power efficient. It is able to handleconcept-drift and concept-evolution due to its evolving nature, which means that, not only rules/classes can be updated, but new classes can be created as new concepts emerge from thedata. It can perform fuzzy classification/soft-labeling, which is preferred over traditional crisp classification in many areas of application. The algorithm was validated with an industrial pilotplant, where online calculated period and amplitude of control signal were used as input to a fault diagnosis application. The approach, however, is generic and can be applied to differentproblems and with much higher dimensional inputs. The results obtained from the real data are very significant. ",
author = "Costa, {Bruno Sielly Jales} and Bezerra, {Clauber Gomes} and Guedes, {Luiz Affonso} and Angelov, {Plamen Parvanov}",
note = "{\textcopyright}2016 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.",
year = "2016",
month = jul
day = "24",
doi = "10.1109/FUZZ-IEEE.2016.7737668",
language = "English",
isbn = "9781509006250",
pages = "58--63",
booktitle = "2016 IEEE International Conference on Fuzzy Systems (FUZZ)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Unsupervised classification of data streams based on typicality and eccentricity data analytics

AU - Costa, Bruno Sielly Jales

AU - Bezerra, Clauber Gomes

AU - Guedes, Luiz Affonso

AU - Angelov, Plamen Parvanov

N1 - ©2016 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.

PY - 2016/7/24

Y1 - 2016/7/24

N2 - In this paper, we propose a novel approach to unsupervised and online data classification. The algorithm is based on the statistical analysis of selected features and development of a self-evolving fuzzy-rule-basis. It starts learning from an empty rule basis and, instead of offline training, it learns “on-the-fly”.It is free of parameters and, thus, fuzzy rules, number, size or radius of the classes do not need to be pre-defined. It is very suitable for the classification of online data streams with realtimeconstraints. The past data do not need to be stored in memory, since that the algorithm is recursive, which makes it memory and computational power efficient. It is able to handleconcept-drift and concept-evolution due to its evolving nature, which means that, not only rules/classes can be updated, but new classes can be created as new concepts emerge from thedata. It can perform fuzzy classification/soft-labeling, which is preferred over traditional crisp classification in many areas of application. The algorithm was validated with an industrial pilotplant, where online calculated period and amplitude of control signal were used as input to a fault diagnosis application. The approach, however, is generic and can be applied to differentproblems and with much higher dimensional inputs. The results obtained from the real data are very significant.

AB - In this paper, we propose a novel approach to unsupervised and online data classification. The algorithm is based on the statistical analysis of selected features and development of a self-evolving fuzzy-rule-basis. It starts learning from an empty rule basis and, instead of offline training, it learns “on-the-fly”.It is free of parameters and, thus, fuzzy rules, number, size or radius of the classes do not need to be pre-defined. It is very suitable for the classification of online data streams with realtimeconstraints. The past data do not need to be stored in memory, since that the algorithm is recursive, which makes it memory and computational power efficient. It is able to handleconcept-drift and concept-evolution due to its evolving nature, which means that, not only rules/classes can be updated, but new classes can be created as new concepts emerge from thedata. It can perform fuzzy classification/soft-labeling, which is preferred over traditional crisp classification in many areas of application. The algorithm was validated with an industrial pilotplant, where online calculated period and amplitude of control signal were used as input to a fault diagnosis application. The approach, however, is generic and can be applied to differentproblems and with much higher dimensional inputs. The results obtained from the real data are very significant.

U2 - 10.1109/FUZZ-IEEE.2016.7737668

DO - 10.1109/FUZZ-IEEE.2016.7737668

M3 - Conference contribution/Paper

SN - 9781509006250

SP - 58

EP - 63

BT - 2016 IEEE International Conference on Fuzzy Systems (FUZZ)

PB - IEEE

CY - Vancouver Canada

ER -