Pill Shape Classification using Imbalanced Data with Human-Machine Hybrid Explainable Model

摘要

A Human Machine Hybrid (HMH) pill shape classification system uses a decision tree with interpretable metrics. The disclosed approach for pill shape classification requires human intervention for determining the meta-classes and variables used. The creation of decision boundaries is accomplished with machine learning (ML) algorithms. Scatter plots are manually inspected to find candidate pairs of variables and potential meta-classes.