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Automated machine learning (AutoML) systems propose an end-to-end solution toa given machine learning problem, creating either fixed or flexible pipelines.Fixed pipelines are task independent constructs: their general compositionremains the same, regardless of the data. In contrast, the structure offlexible pipelines varies depending on the input, making them finely tailoredto individual tasks. However, flexible pipelines can be structurallyovercomplicated and have poor explainability. We propose the EVOSA approachthat compensates for the negative points of flexible pipelines by incorporatinga sensitivity analysis which increases the robustness and interpretability ofthe flexible solutions. EVOSA quantitatively estimates positive and negativeimpact of an edge or a node on a pipeline graph, and feeds this information tothe evolutionary AutoML optimizer. The correctness and efficiency of EVOSA wasvalidated in tabular, multimodal and computer vision tasks, suggestinggeneralizability of the proposed approach across domains.

Integration Of Evolutionary Automated Machine Learning With Structural Sensitivity Analysis For Composite Pipelines