Empirical estimation of various data stream mining methods
by Ritesh Srivastava; Veena Mittal
International Journal of Computational Intelligence Studies (IJCISTUDIES), Vol. 10, No. 1, 2021

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.

Online publication date: Wed, 31-Mar-2021

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