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Found 4 papers, 3 papers with code
Probabilistic Truly Unordered Rule Sets
To address these shortcomings, we propose TURS, for Truly Unordered Rule Sets.
Truly Unordered Probabilistic Rule Sets for Multi-class Classification
Still, existing methods have several shortcomings: 1) most recent methods require a binary feature matrix as input, while learning rules directly from numeric variables is understudied; 2) existing methods impose orders among rules, either explicitly or implicitly, which harms interpretability; and 3) currently no method exists for learning probabilistic rule sets for multi-class target variables (there is only one for probabilistic rule lists).
Estimating Conditional Mutual Information for Discrete-Continuous Mixtures using Multi-Dimensional Adaptive Histograms
Further, we show that CMI can be consistently estimated for discrete-continuous mixture variables by learning an adaptive histogram model.
Causal Discovery
Information Theory
Information Theory
Applications
Unsupervised Discretization by Two-dimensional MDL-based Histogram
As the flexibility of our model class comes at the cost of a vast search space, we introduce a heuristic algorithm, named PALM, which Partitions each dimension ALternately and then Merges neighboring regions, all using the MDL principle.
