A Domain-Specific Tool for the Creation of Machine Learning Models with Imbalanced Datasets

In this paper, we introduce a domain-specific tool designed to streamline the development of deep learning models for industrial applications, particularly those facing the challenges posed by imbalanced datasets. The advent of pervasive computing has allowed the creation of new smart services based on machine learning, yet the complexity of effectively harnessing data from IoT devices remains a challenge. The data characteristics - ranging from noise and scarcity to a lack of labels and imbalance - present significant obstacles for their utilization. A focal point of this research is the issue of class imbalance, which significantly impedes the achievement of accurate predictive models, especially in critical applications like anomaly detection. Our paper presents a solution through the development of a specialized tool that integrates state-of-the-art techniques and metrics specifically designed to deal with class imbalance. This tool also adapts to various constraints set forth by domain experts, thereby facilitating the creation of optimized models suited to specific industrial needs. To demonstrate its practicality and effectiveness, we apply our tool in a case study focusing on the detection of motor malfunctions in the context of Industry 4.0, in collaboration with Schneider Electric.