CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2015-11-11
Cited: 0
Clicked: 7245
Omid Abbaszadeh, Ali Amiri, Ali Reza Khanteymoori. An ensemble method for data stream classification in the presence of concept drift[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(12): 1059-1068.
@article{title="An ensemble method for data stream classification in the presence of concept drift",
author="Omid Abbaszadeh, Ali Amiri, Ali Reza Khanteymoori",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="16",
number="12",
pages="1059-1068",
year="2015",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1400398"
}
%0 Journal Article
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%A Omid Abbaszadeh
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%A Ali Reza Khanteymoori
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%N 12
%P 1059-1068
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1400398
TY - JOUR
T1 - An ensemble method for data stream classification in the presence of concept drift
A1 - Omid Abbaszadeh
A1 - Ali Amiri
A1 - Ali Reza Khanteymoori
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 16
IS - 12
SP - 1059
EP - 1068
%@ 2095-9184
Y1 - 2015
PB - Zhejiang University Press & Springer
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DOI - 10.1631/FITEE.1400398
Abstract: One recent area of interest in computer science is data stream management and processing. By ‘data stream’, we refer to continuous and rapidly generated packages of data. Specific features of data streams are immense volume, high production rate, limited data processing time, and data concept drift; these features differentiate the data stream from standard types of data. An issue for the data stream is classification of input data. A novel ensemble classifier is proposed in this paper. The classifier uses base classifiers of two weighting functions under different data input conditions. In addition, a new method is used to determine drift, which emphasizes the precision of the algorithm. Another characteristic of the proposed method is removal of different numbers of the base classifiers based on their quality. Implementation of a weighting mechanism to the base classifiers at the decision-making stage is another advantage of the algorithm. This facilitates adaptability when drifts take place, which leads to classifiers with higher efficiency. Furthermore, the proposed method is tested on a set of standard data and the results confirm higher accuracy compared to available ensemble classifiers and single classifiers. In addition, in some cases the proposed classifier is faster and needs less storage space.
The paper discusses an interesting problem of data stream concept drifting. The paper uses ensemble models to handle continuous data streams, and a new weighting schema to drop outdated classifiers.
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