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Online Methods of Learning in Occurrence of Concept Drift

by Veena Mittal, Indu Kashyap
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 117 - Number 13
Year of Publication: 2015
Authors: Veena Mittal, Indu Kashyap
10.5120/20614-3280

Veena Mittal, Indu Kashyap . Online Methods of Learning in Occurrence of Concept Drift. International Journal of Computer Applications. 117, 13 ( May 2015), 18-22. DOI=10.5120/20614-3280

@article{ 10.5120/20614-3280,
author = { Veena Mittal, Indu Kashyap },
title = { Online Methods of Learning in Occurrence of Concept Drift },
journal = { International Journal of Computer Applications },
issue_date = { May 2015 },
volume = { 117 },
number = { 13 },
month = { May },
year = { 2015 },
issn = { 0975-8887 },
pages = { 18-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume117/number13/20614-3280/ },
doi = { 10.5120/20614-3280 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:59:18.222502+05:30
%A Veena Mittal
%A Indu Kashyap
%T Online Methods of Learning in Occurrence of Concept Drift
%J International Journal of Computer Applications
%@ 0975-8887
%V 117
%N 13
%P 18-22
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Due to potentially large number of applications of real-time data stream mining in scientific and business analysis, the real-time data streams mining has drawn attention of many researchers who are working in the area of machine learning and data mining. In many cases, for real-time data stream mining online learning is used. Environments that require online learning are non-stationary and whose underlying distributions may change over time i. e. concept drift, because of which mining of real- time data streams with concept drifts is quite challenging. However, ensemble methods have been suggested for this particular situation. This paper reviews various online methods of drift detection. We also present some results of our experiments that show the comparison of some online drift detection (concept drift) methods.

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Index Terms

Computer Science
Information Sciences

Keywords

Concept Drifts Drift detection algorithms Online methods of learning.

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