Stable classification by components for interpretable machine learning

Abstract

The present disclosure relates to a stable classification by components (SCBC) data processing architecture, configured to classify input data into one or more classes, comprising: a component detection module configured to compare the input data to a set of detection components, representing data patterns relevant for the classification, and determine a detection probability for each detection component based on the comparison. The SCBC data processing architecture further comprises a probabilistic reasoning module configured to compute one or more class prediction probabilities for the one or more classes based on the determined detection probabilities, a set of class-specific prior probabilities for the determined detection probabilities, and a set of class-specific reasoning probabilities for the determined detection probabilities. Application scenarios include medical and pharmaceutical applications, as well as healthcare in general such as interpretable and secure diagnosis and treatment recommendation systems. Related SCBC data processing system, methods and computer programs are also disclosed, as well as corresponding model training methods and systems.

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