Evolutionary Automated Machine Learning for Light-Weight Multi-Modal Pipelines

The effective modelling for multi-modal data is still one of challenging problems in modern machine learning. The most existing tools for automated machine learning avoids the multi-modality, and a few others use computationally expensive end-to-end deep models, that can be redunant for a lot of cases. In this paper, we propose the evolutionary approach for automated design of light-weight multi-modal pipelines. It is based an graph-based representation of pipeline and simplification of over-complicated solutions. The experiments with AutoML Multi-modal Benchmark confirms that the proposed approach allow achieving the competitive performance against more complicated state-of-the-art solutions.