Tailored Military Recruitment through Machine Learning Algorithms

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.