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International Journal of Intelligent Systems and Applications(IJISA)

ISSN: 2074-904X (Print), ISSN: 2074-9058 (Online)

Published By: MECS Press

IJISA Vol.8, No.12, Dec. 2016

Empirical Study of Impact of Various Concept Drifts in Data Stream Mining Methods

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Author(s)

Veena Mittal, Indu Kashyap

Index Terms

Concept drift;online learning;data stream mining;drift detection;ensembles

Abstract

In the real world, most of the applications are inherently dynamic in nature i.e. their underlying data distribution changes with time. As a result, the concept drifts occur very frequently in the data stream. Concept drifts in data stream increase the challenges in learning as well, it also significantly decreases the accuracy of the classifier. However, recently many algorithms have been proposed that exclusively designed for data stream mining while considering drifting concept in the data stream.This paper presents an empirical evaluation of these algorithms on datasets having four possible types of concept drifts namely; sudden, gradual, incremental, and recurring drifts.

Cite This Paper

Veena Mittal, Indu Kashyap,"Empirical Study of Impact of Various Concept Drifts in Data Stream Mining Methods", International Journal of Intelligent Systems and Applications(IJISA), Vol.8, No.12, pp.65-72, 2016. DOI: 10.5815/ijisa.2016.12.08

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