A semi-supervised learning-aided evolutionary approach to occupational safety improvement

Worldwide, four people die every minute as a consequence of illnesses and accidents at work. This considerable number makes occupational safety an important research area aimed at obtaining safer and safer workplaces. This paper presents a semi-supervised learning-aided evolutionary approach to improve occupational safety by classifying workers depending on their own risk perception for the task assigned. More in detail, a semi-supervised learning phase is carried out to initialize a good population of a non-dominated sorting genetic algorithm (NSGA-II). Each chromosome of the population represents a pair of classifiers: one determines a worker's risk perception with respect to a task, the other determines the level of caution of the same worker for the same task. Learning from constraints reinforces the initial training performance. The best Pareto-optimal solution to the problem is selected by means of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The proposed framework was tested on real-world data gathered through a website purposely developed. Results showed a good performance of the obtained classifiers, thus validating the effectiveness of the proposed approach in supporting the decision-maker in critical job assignment problems, where risks are a serious threat to the workers' health.