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Data stream classification with ant colony optimisation

Helal, Ayah, Otero, Fernando E.B. (2022) Data stream classification with ant colony optimisation. International Journal of Intelligent Systems, . ISSN 0884-8173. (doi:10.1002/int.22809) (KAR id:92827)

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Data stream mining has recently emerged in response to the rapidly increasing continuous data generation. While the majority of Ant Colony Optimisation (ACO) rule induction algorithms have proved to be successful in producing both accurate and comprehensive classification models in nonstreaming (batch) settings, currently ACO-based algorithms for classification problems are not suited to be applied to data stream mining. One of the main challenges is the iterative nature of ACO algorithms, where many procedures—for example, heuristic calculation, selection of continuous attributes, pruning—require multiple passes through the data to create a model. In this paper, we present a new ACO-based algorithm for data stream classification. The proposed algorithm, called Stream Ant-Miner (sAnt-Miner), uses a novel hybrid pheromone model combining both a traditional construction graph and solution archives models to efficiently handle a large number of mixed-type (nominal and continuous) attributes directly without the need for additional procedures, reducing the computational time required to complete an iteration of the algorithm. Our results show that sAnt-Miner produces statistically significant concise models compared with state-of-the-art rule induction data stream algorithms, without negative effects on their predictive accuracy.

Item Type: Article
DOI/Identification number: 10.1002/int.22809
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Fernando Otero
Date Deposited: 24 Jan 2022 11:33 UTC
Last Modified: 09 Jan 2023 00:00 UTC
Resource URI: (The current URI for this page, for reference purposes)
Otero, Fernando E.B.:
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