Multiobjective Optimization

In this paper, we aim to introduce a novel method for explaining time-series classification, leveraging the capabilities of ChatGPT to enhance the interpretability of results and foster a deeper understanding of feature contributions within time-series data.

Tiny edge systems used in IoT devices, wearables or smart textiles are characterized by the need of processing complex sensor data streams under various device constraints. Due to the high number of constraints and the complexity of the optimization of the hyper-parameter space for machine learning based processing, genetic algorithms (GAs) seem to be a perfect fit to enable AutoML for those embedded devices. We introduce a novel Genetic Algorithm (GA) customized for AutoML tasks, addressing the unique challenges posed by highly embedded machine learning domains.