Developing a machine learning-based evaluation system for the recruitment of maritime professionals

Abstract

The maritime sector predominantly relies on subjective evaluations of seafarers' skills and experience in conventional recruiting procedures. Nevertheless, subjective evaluation methods are highly susceptible to biases and inconsistencies. This study proposes a novel recruitment process within the maritime industry by merging psychological tests and machine learning methods in the recruitment process. Using psychological tests such as MMPI-I as features in machine learning methods for the recruitment process represents a step-change approach within the industry to promote more objective assessments of maritime professionals during recruitment processes and identify suitable candidates based on data from same-rank maritime professionals. This new methodology contributes innovatively to traditional maritime sector recruitment methods and potentially addresses a significant gap in the existing literature. The proposed methodology aims to predict future values by analysing existing data sets. Data were collected from 183 volunteer cadets from different backgrounds using an application form and the MMPI-I Personality Inventory. The dataset was classified using several machine learning algorithms, and their performance metrics were compared. The top five classification algorithms (Decision tree, PNN, random forest, gradient boost trees, and naive Bayes) with the best performance were evaluated, and the most accurate classification performance was achieved with the GBT algorithm. The results show that the highest values are for Gradient Boosted Trees (86%) and Random Forest (80%). The GBT algorithm has scored higher values for other metrics as well. The findings of this study indicate that it is possible to train algorithms that can adequately forecast the credentials and appropriateness of marine recruits. Through more development, these data-driven solutions could enhance existing subjective recruitment practices. This approach could improve the objectivity and prediction accuracy of marine recruiting processes, hence facilitating the selection of highly qualified individuals