RED-GENE: An Evolutionary Game Theoretic Approach to Adaptive Data Stream Classification

Ghomeshi, Hossein and Gaber, Mohamed Medhat and Kovalchuk., Yevgeniya (2019) RED-GENE: An Evolutionary Game Theoretic Approach to Adaptive Data Stream Classification. IEEE Access. p. 1. ISSN 2169-3536

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Abstract

The extensive growth of digital technologies such as the Internet of Things (IoT), social media networks and forecasting systems has led to new challenges regarding computational complexity and big data mining. The classification task in such applications is not trivial due to the high volume of related data and limited time available for the task. It is particularly difficult when dealing with data streams, where each instance of data is typically processed once on its arrival (i.e. online) while the underlying data distribution often changes due to the changing environment. In this paper, we propose a novel ensemble-based framework called Replicator Dynamics & Genetic Algorithms Approach (RED-GENE) for effective data stream classification in the context of changing environment leading to concept drifts (i.e. evolution of data streams). RED-GENE employs three novel Replicator Dynamics (RD) strategies along with a Genetic Algorithm (GA) optimisation technique to flexibly adapt to different types of concept drifts when performing data stream classification tasks. The proposed framework works as follows. First, a set of random feature combinations is drawn from a given pool of features of the target data stream to create different classification types. Next, RD is used to allow the classification types achieving higher classification accuracy to grow and those with lower accuracy to shrink. A modified version of the classic GA is then employed to optimise the randomly drawn combinations of features in each classification type. The proposed framework was tested using nine data streams (including both real-world and synthetic datasets) to investigate different variations of the proposed framework and compare its performance to other state-of-the-art algorithms using immediate and delayed prequential evaluation methods. The results demonstrated that the proposed framework can provide the best accuracy on average when comparing to five other state-of-the-art algorithms.

Item Type: Article
Identification Number: https://doi.org/10.1109/ACCESS.2019.2954993
Date: 21 November 2019
Subjects: G700 Artificial Intelligence
Divisions: Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology > Cyber Security
Depositing User: Mohamed Gaber
Date Deposited: 24 Nov 2019 20:33
Last Modified: 11 Dec 2019 11:57
URI: http://www.open-access.bcu.ac.uk/id/eprint/8507

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