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Machine learning (ML) research has yielded powerful tools for trainingaccurate prediction models despite complex multivariate associations (e.g.interactions and heterogeneity). In fields such as medicine, improvedinterpretability of ML modeling is required for knowledge discovery,accountability, and fairness. Rule-based ML approaches such as LearningClassifier Systems (LCSs) strike a balance between predictive performance andinterpretability in complex, noisy domains. This work introduces the LCSDiscovery and Visualization Environment (LCS-DIVE), an automated LCS modelinterpretation pipeline for complex biomedical classification. LCS-DIVEconducts modeling using a new scikit-learn implementation of ExSTraCS, an LCSdesigned to overcome noise and scalability in biomedical data mining yieldinghuman readable IF:THEN rules as well as feature-tracking scores for eachtraining sample. LCS-DIVE leverages feature-tracking scores and/or rules toautomatically guide characterization of (1) feature importance (2) underlyingadditive, epistatic, and/or heterogeneous patterns of association, and (3)model-driven heterogeneous instance subgroups via clustering, visualizationgeneration, and cluster interrogation. LCS-DIVE was evaluated over a diverseset of simulated genetic and benchmark datasets encoding a variety of complexmultivariate associations, demonstrating its ability to differentiate betweenthem and then applied to characterize associations within a real-world study ofpancreatic cancer.

LCS-DIVE: An Automated Rule-based Machine Learning Visualization Pipeline for Characterizing Complex Associations in Classification