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Dynamically evolving fuzzy classifier for real-time classification of data streams

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

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Standard

Dynamically evolving fuzzy classifier for real-time classification of data streams. / Dutta Baruah, Rashmi; Angelov, Plamen; Baruah, Diganta.

Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on. IEEE, 2014. p. 383-389.

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

Harvard

Dutta Baruah, R, Angelov, P & Baruah, D 2014, Dynamically evolving fuzzy classifier for real-time classification of data streams. in Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on. IEEE, pp. 383-389, 2014 , Beijing, China, 6/07/14. https://doi.org/10.1109/FUZZ-IEEE.2014.6891758

APA

Dutta Baruah, R., Angelov, P., & Baruah, D. (2014). Dynamically evolving fuzzy classifier for real-time classification of data streams. In Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on (pp. 383-389). IEEE. https://doi.org/10.1109/FUZZ-IEEE.2014.6891758

Vancouver

Dutta Baruah R, Angelov P, Baruah D. Dynamically evolving fuzzy classifier for real-time classification of data streams. In Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on. IEEE. 2014. p. 383-389 https://doi.org/10.1109/FUZZ-IEEE.2014.6891758

Author

Dutta Baruah, Rashmi ; Angelov, Plamen ; Baruah, Diganta. / Dynamically evolving fuzzy classifier for real-time classification of data streams. Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on. IEEE, 2014. pp. 383-389

Bibtex

@inproceedings{a46fe045bfdc4618934a175064d05329,
title = "Dynamically evolving fuzzy classifier for real-time classification of data streams",
abstract = "In this paper, a novel evolving fuzzy rule-based classifier is presented. The proposed classifier addresses the three fundamental issues of data stream learning, viz., computational efficiency in terms of processing time and memory requirements, adaptive to changes, and robustness to noise. Though, there are several online classifiers available, most of them do not take into account all the three issues simultaneously. The newly proposed classifier is inherently adaptive and can attend to any minute changes as it learns the rules in online manner by considering each incoming example. However, it should be emphasized that it can easily distinguish noise from new concepts and automatically handles noise. The performance of the classifier is evaluated using real-life data with evolving characteristic and compared with state-of-the-art adaptive classifiers. The experimental results show that the classifier attains a simple model in terms of number of rules. Further, the memory requirements and processing time per sample does not increase linearly with the progress of the stream. Thus, the classifier is capable of performing both prediction and model update in real-time in a streaming environment.",
author = "{Dutta Baruah}, Rashmi and Plamen Angelov and Diganta Baruah",
year = "2014",
month = jul
day = "6",
doi = "10.1109/FUZZ-IEEE.2014.6891758",
language = "English",
isbn = "9781479920730",
pages = "383--389",
booktitle = "Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on",
publisher = "IEEE",
note = "2014 ; Conference date: 06-07-2014 Through 11-07-2014",

}

RIS

TY - GEN

T1 - Dynamically evolving fuzzy classifier for real-time classification of data streams

AU - Dutta Baruah, Rashmi

AU - Angelov, Plamen

AU - Baruah, Diganta

PY - 2014/7/6

Y1 - 2014/7/6

N2 - In this paper, a novel evolving fuzzy rule-based classifier is presented. The proposed classifier addresses the three fundamental issues of data stream learning, viz., computational efficiency in terms of processing time and memory requirements, adaptive to changes, and robustness to noise. Though, there are several online classifiers available, most of them do not take into account all the three issues simultaneously. The newly proposed classifier is inherently adaptive and can attend to any minute changes as it learns the rules in online manner by considering each incoming example. However, it should be emphasized that it can easily distinguish noise from new concepts and automatically handles noise. The performance of the classifier is evaluated using real-life data with evolving characteristic and compared with state-of-the-art adaptive classifiers. The experimental results show that the classifier attains a simple model in terms of number of rules. Further, the memory requirements and processing time per sample does not increase linearly with the progress of the stream. Thus, the classifier is capable of performing both prediction and model update in real-time in a streaming environment.

AB - In this paper, a novel evolving fuzzy rule-based classifier is presented. The proposed classifier addresses the three fundamental issues of data stream learning, viz., computational efficiency in terms of processing time and memory requirements, adaptive to changes, and robustness to noise. Though, there are several online classifiers available, most of them do not take into account all the three issues simultaneously. The newly proposed classifier is inherently adaptive and can attend to any minute changes as it learns the rules in online manner by considering each incoming example. However, it should be emphasized that it can easily distinguish noise from new concepts and automatically handles noise. The performance of the classifier is evaluated using real-life data with evolving characteristic and compared with state-of-the-art adaptive classifiers. The experimental results show that the classifier attains a simple model in terms of number of rules. Further, the memory requirements and processing time per sample does not increase linearly with the progress of the stream. Thus, the classifier is capable of performing both prediction and model update in real-time in a streaming environment.

U2 - 10.1109/FUZZ-IEEE.2014.6891758

DO - 10.1109/FUZZ-IEEE.2014.6891758

M3 - Conference contribution/Paper

SN - 9781479920730

SP - 383

EP - 389

BT - Fuzzy Systems (FUZZ-IEEE), 2014 IEEE International Conference on

PB - IEEE

T2 - 2014

Y2 - 6 July 2014 through 11 July 2014

ER -