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Published, Version of Record (VoR)CC BY V4.0, Open
Journal article
A Multi-Agent Approach to Binary Classification Using Swarm Intelligence
Future internet, v 15(1)
01 Jan 2023
Featured in Collection : UN Sustainable Development Goals @ Drexel
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.
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Details
- Title
- A Multi-Agent Approach to Binary Classification Using Swarm Intelligence
- Creators
- Sean Grimes - Drexel Univ, Dept Comp Sci, Philadelphia, PA 19104 USADavid E. Breen - Drexel University
- Publication Details
- Future internet, v 15(1)
- Publisher
- Mdpi
- Number of pages
- 27
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000917538400001
- Scopus ID
- 2-s2.0-85146815730
- Other Identifier
- 991020547445104721
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- Web of Science research areas
- Computer Science, Information Systems