A Review on Dynamic Concept Drift

International Journal of Computer Science and Engineering

© 2016 by SSRG - IJCSE Journal

Volume 3 Issue 10

Year of Publication : 2016

Authors : D.Kishore Babu, Dr. Y.Ramadevi, Dr.K.V.Ramana

10.14445/23488387/IJCSE-V3I10P113

How to Cite?

D.Kishore Babu, Dr. Y.Ramadevi, Dr.K.V.Ramana, "A Review on Dynamic Concept Drift," SSRG International Journal of Computer Science and Engineering , vol. 3,  no. 10, pp. 41-47, 2016. Crossref, https://doi.org/10.14445/23488387/IJCSE-V3I10P113

Abstract:

In the real world concepts are frequently not stable but change with time. Typical examples of this are weather prediction rules and customers' preferences. The underlying data distribution may change as well. Over and over again these changes make the model built on old data inconsistent with the new data, and regular updating of the model is necessary. This problem, known as concept drift, complicates the task of learning a model from data and requires special approaches, different from commonly used techniques, which treat arriving instances as equally important contributors to the final concept. Dynamic concept drift is one of the techniques which will handle the concept drift effectively by using ensembles. Dynamic concept drift with Naïve base classifier is finest one.

Keywords:

concept drift, ensembles, dynamic integration, dynamic weighted majority

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