Home > Research > Publications & Outputs > Evolving fuzzy set-based and cloud-based unsupe...

Electronic data

Links

Text available via DOI:

View graph of relations

Evolving fuzzy set-based and cloud-based unsupervised classifiers for spam detection

Research output: Contribution to journalJournal articlepeer-review

Published

Standard

Evolving fuzzy set-based and cloud-based unsupervised classifiers for spam detection. / Soares, Eduardo; Garcia, Cristiano; Poucas, Ricardo; Camargo, Heloisa; Leite, Daniel.

In: IEEE Latin America Transactions, Vol. 17, No. 9, 8931138, 30.09.2019, p. 1449-1457.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Soares, E, Garcia, C, Poucas, R, Camargo, H & Leite, D 2019, 'Evolving fuzzy set-based and cloud-based unsupervised classifiers for spam detection', IEEE Latin America Transactions, vol. 17, no. 9, 8931138, pp. 1449-1457. https://doi.org/10.1109/TLA.2019.8931138

APA

Soares, E., Garcia, C., Poucas, R., Camargo, H., & Leite, D. (2019). Evolving fuzzy set-based and cloud-based unsupervised classifiers for spam detection. IEEE Latin America Transactions, 17(9), 1449-1457. [8931138]. https://doi.org/10.1109/TLA.2019.8931138

Vancouver

Soares E, Garcia C, Poucas R, Camargo H, Leite D. Evolving fuzzy set-based and cloud-based unsupervised classifiers for spam detection. IEEE Latin America Transactions. 2019 Sep 30;17(9):1449-1457. 8931138. https://doi.org/10.1109/TLA.2019.8931138

Author

Soares, Eduardo ; Garcia, Cristiano ; Poucas, Ricardo ; Camargo, Heloisa ; Leite, Daniel. / Evolving fuzzy set-based and cloud-based unsupervised classifiers for spam detection. In: IEEE Latin America Transactions. 2019 ; Vol. 17, No. 9. pp. 1449-1457.

Bibtex

@article{33d5640452124b0da8da39ee0cec147b,
title = "Evolving fuzzy set-based and cloud-based unsupervised classifiers for spam detection",
abstract = "Technological advancements has made individuals and organizations more dependent on e-mails to communicate and share information. The increasing use of e-mails has led to an increased production of unsolicited commercial messages, known as spam. Spam classification systems able to self-adapt over time, with no human intervention, are rare. Adaptation is interesting as spams vary over time due to the use of different message-masking techniques. Moreover, classification models that handle large volumes of data are essential. Evolving intelligent systems are able to adapt their parameters and structure according to the data stream. This study applies the evolving methods TEDA (Typicality and Eccentricity based Data Analytics) and FBeM (Fuzzy Set-Based Evolving Modeling) for online unsupervised classification of spams. TEDA and FBeM are compared in terms of accuracy, model compactness, and processing time. For dimensionality reduction, a non-parametric Spearman-correlation-based feature selection method is employed. A dataset containing 25,745 samples, being 7,830 spams and 17,915 legitimate e-mails, is considered. 711 features extracted from an e-mail server describe each sample.",
keywords = "Clustering, Data Streams, Evolving Intelligent Systems, Spam Detection, Unsupervised Classification",
author = "Eduardo Soares and Cristiano Garcia and Ricardo Poucas and Heloisa Camargo and Daniel Leite",
year = "2019",
month = sep,
day = "30",
doi = "10.1109/TLA.2019.8931138",
language = "Portuguese",
volume = "17",
pages = "1449--1457",
journal = "IEEE Latin America Transactions",
issn = "1548-0992",
publisher = "IEEE Computer Society Press",
number = "9",

}

RIS

TY - JOUR

T1 - Evolving fuzzy set-based and cloud-based unsupervised classifiers for spam detection

AU - Soares, Eduardo

AU - Garcia, Cristiano

AU - Poucas, Ricardo

AU - Camargo, Heloisa

AU - Leite, Daniel

PY - 2019/9/30

Y1 - 2019/9/30

N2 - Technological advancements has made individuals and organizations more dependent on e-mails to communicate and share information. The increasing use of e-mails has led to an increased production of unsolicited commercial messages, known as spam. Spam classification systems able to self-adapt over time, with no human intervention, are rare. Adaptation is interesting as spams vary over time due to the use of different message-masking techniques. Moreover, classification models that handle large volumes of data are essential. Evolving intelligent systems are able to adapt their parameters and structure according to the data stream. This study applies the evolving methods TEDA (Typicality and Eccentricity based Data Analytics) and FBeM (Fuzzy Set-Based Evolving Modeling) for online unsupervised classification of spams. TEDA and FBeM are compared in terms of accuracy, model compactness, and processing time. For dimensionality reduction, a non-parametric Spearman-correlation-based feature selection method is employed. A dataset containing 25,745 samples, being 7,830 spams and 17,915 legitimate e-mails, is considered. 711 features extracted from an e-mail server describe each sample.

AB - Technological advancements has made individuals and organizations more dependent on e-mails to communicate and share information. The increasing use of e-mails has led to an increased production of unsolicited commercial messages, known as spam. Spam classification systems able to self-adapt over time, with no human intervention, are rare. Adaptation is interesting as spams vary over time due to the use of different message-masking techniques. Moreover, classification models that handle large volumes of data are essential. Evolving intelligent systems are able to adapt their parameters and structure according to the data stream. This study applies the evolving methods TEDA (Typicality and Eccentricity based Data Analytics) and FBeM (Fuzzy Set-Based Evolving Modeling) for online unsupervised classification of spams. TEDA and FBeM are compared in terms of accuracy, model compactness, and processing time. For dimensionality reduction, a non-parametric Spearman-correlation-based feature selection method is employed. A dataset containing 25,745 samples, being 7,830 spams and 17,915 legitimate e-mails, is considered. 711 features extracted from an e-mail server describe each sample.

KW - Clustering

KW - Data Streams

KW - Evolving Intelligent Systems

KW - Spam Detection

KW - Unsupervised Classification

U2 - 10.1109/TLA.2019.8931138

DO - 10.1109/TLA.2019.8931138

M3 - Journal article

AN - SCOPUS:85076642436

VL - 17

SP - 1449

EP - 1457

JO - IEEE Latin America Transactions

JF - IEEE Latin America Transactions

SN - 1548-0992

IS - 9

M1 - 8931138

ER -