Theory and rationale of interpretable all-in-one pattern discovery and disentanglement system.

ABSTRACT: In machine learning (ML), association patterns in the data, paths in decision trees, and weights between layers of the neural network are often entangled due to multiple underlying causes, thus masking the pattern-to-source relation, weakening prediction, and defying explanation. This paper presents a revolutionary ML paradigm: pattern discovery and disentanglement (PDD) that disentangles associations and provides an all-in-one knowledge system capable of (a) disentangling patterns to associate with distinct primary sources; (b) discovering rare/imbalanced groups, detecting anomalies and rectifying discrepancies to improve class association, pattern and entity clustering; and (c) organizing knowledge for statistically supported interpretability for causal exploration. Results from case stu

SUBMITTER: Wong AKC 

PROVIDER: S-EPMC10203344 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature