Tailored Military Recruitment through Machine Learning Algorithms

Robert Bryce; Ryuichi Ueno; Christopher Mcdonald; Dragos Calitoiu

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Paper

Tailored Military Recruitment through Machine Learning Algorithms

Topics: Clustering, Classification and Regression; Deep Metric Learning Methods; Meta-Learning and Deep Networks

In Proceedings of the 2nd International Conference on Deep Learning Theory and Applications DeLTA - Volume 1, 87-92, 2021

Authors: Robert Bryce ; Ryuichi Ueno ; Christopher Mcdonald and Dragos Calitoiu

Affiliation: Director General Military Personnel Research and Analysis, Department of National Defence, 101 Colonel By Drive, Ottawa, Canada

Keyword(s): Machine Learning, Ensemble Learning, Workforce Analytics, Recruitment.

Abstract: Identifying postal codes with the highest recruiting potential corresponding to the desired profile for a military occupation can be achieved by using the demographics of the population living in that postal code and the location of both the successful and unsuccessful applicants. Selecting N individuals with the highest probability to be enrolled from a population living in untapped postal codes can be done by ranking the postal codes using a machine learning predictive model. Three such models are presented in this paper: a logistic regression, a multi-layer perceptron and a deep neural network. The key contribution of this paper is an algorithm that combines these models, benefiting from the performance of each of them, producing a desired selection of postal codes. This selection can be converted into N prospects living in these areas. A dataset consisting of the applications to the Canadian Armed Forces (CAF) is used to illustrate the methodology proposed.

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Paper citation in several formats:

Bryce, R.; Ueno, R.; Mcdonald, C. and Calitoiu, D. (2021). Tailored Military Recruitment through Machine Learning Algorithms. In Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - DeLTA; ISBN 978-989-758-526-5; ISSN 2184-9277, SciTePress, pages 87-92. DOI: 10.5220/0010506500870092

@conference{delta21,
author={Robert Bryce. and Ryuichi Ueno. and Christopher Mcdonald. and Dragos Calitoiu.},
title={Tailored Military Recruitment through Machine Learning Algorithms},
booktitle={Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - DeLTA},
year={2021},
pages={87-92},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010506500870092},
isbn={978-989-758-526-5},
issn={2184-9277},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Deep Learning Theory and Applications - DeLTA
TI - Tailored Military Recruitment through Machine Learning Algorithms
SN - 978-989-758-526-5
IS - 2184-9277
AU - Bryce, R.
AU - Ueno, R.
AU - Mcdonald, C.
AU - Calitoiu, D.
PY - 2021
SP - 87
EP - 92
DO - 10.5220/0010506500870092
PB - SciTePress