Abstract
The team formation problem has existed for many years in various guises. One important problem in the team formation problem is to produce small teams that have a required set of skills. We propose a framework that incorporates machine learning to predict unobserved links between collaborators, alongside improved Steiner tree problems to form small teams to cover given tasks. Our framework not only considers size of the team but also how likely are team members are going to collaborate with each other. The results show that this model consistently returns smaller collaborative teams.
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Acknowledgement
This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) and is co-funded under the European Regional Development Fund under Grant Number 13/RC/2077.
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Keane, P., Ghaffar, F., Malone, D. (2020). Using Machine Learning to Predict Links and Improve Steiner Tree Solutions to Team Formation Problems. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 882. Springer, Cham. https://doi.org/10.1007/978-3-030-36683-4_79
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DOI: https://doi.org/10.1007/978-3-030-36683-4_79
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