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A Multi-Agent Approach to Binary Classification Using Swarm Intelligence

Author

Listed:
  • Sean Grimes

    (Department of Computer Science, Drexel University, Philadelphia, PA 19104, USA)

  • David E. Breen

    (Department of Computer Science, Drexel University, Philadelphia, PA 19104, USA)

Abstract

Wisdom-of-Crowds-Bots (WoC-Bots) are simple, modular agents working together in a multi-agent environment to collectively make binary predictions. The agents represent a knowledge-diverse crowd, with each agent trained on a subset of available information. A honey-bee-derived swarm aggregation mechanism is used to elicit a collective prediction with an associated confidence value from the agents. Due to their multi-agent design, WoC-Bots can be distributed across multiple hardware nodes, include new features without re-training existing agents, and the aggregation mechanism can be used to incorporate predictions from other sources, thus improving overall predictive accuracy of the system. In addition to these advantages, we demonstrate that WoC-Bots are competitive with other top classification methods on three datasets and apply our system to a real-world sports betting problem, producing a consistent return on investment from 1 January 2021 through 15 November 2022 on most major sports.

Suggested Citation

  • Sean Grimes & David E. Breen, 2023. "A Multi-Agent Approach to Binary Classification Using Swarm Intelligence," Future Internet, MDPI, vol. 15(1), pages 1-27, January.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:1:p:36-:d:1033942
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    References listed on IDEAS

    as
    1. Lipowski, Adam & Lipowska, Dorota, 2012. "Roulette-wheel selection via stochastic acceptance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(6), pages 2193-2196.
    2. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    3. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
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