This paper considers the learning of Boolean rules in either disjunctive normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) or conjunctive normal form (CNF, AND-of-ORs) as an interpretable model for classification.
In this paper we consider the problem of building Boolean rule sets in disjunctive normal form (DNF), an interpretable model for binary classification, subject to fairness constraints.
Identifying discrete patterns in binary data is an important dimensionality reduction tool in machine learning and data mining.
We then present a hierarchical partitioning approach that exploits the label hierarchy in large scale problems to divide up the large label space and create smaller sub-problems, which can then be solved independently via the grouping approach.
Low-rank approximations of data matrices are an important dimensionality reduction tool in machine learning and regression analysis.
Decision trees have been a very popular class of predictive models for decades due to their interpretability and good performance on categorical features.