Title: Empirical estimation of various data stream mining methods

Authors: Ritesh Srivastava; Veena Mittal

Addresses: Computer Science and Engineering Department, Galgotias College of Engineering and Technology, Knowledge Park II, Greater Noida, Uttar Pradesh 201310, India ' Computer Science and Engineering Department, Faculty of Engineering and Technology, MRIIRS, Faridabad, India

Abstract: Online learning is done in order to work on dynamic environments in which the concept tends to change with time and the accuracy of classifiers decreases. The current and previous research is done in static environments, but there is a need of a real time data streaming due to the potentially larger number of applications available in the scientific and business domains. There are several methods used in learning in the presence of dynamic environments like single classifier methods such as batch and incremental learning approaches, classification methods with explicit drift detection method, windowing techniques and ensemble approaches. This paper, investigates these approaches for determining the best suitable method among them. We utilised light emitting diode (LED) data generator for evaluating the performance of the methods.

Keywords: concept drifts; online learning; data stream mining; machine learning; classification; drift detection methods.

DOI: 10.1504/IJCISTUDIES.2021.113804

International Journal of Computational Intelligence Studies, 2021 Vol.10 No.1, pp.13 - 26

Received: 25 May 2019
Accepted: 20 Aug 2019
Published online: 31 Mar 2021 *