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 …

Abstract Logical Analysis of Data (LAD) is a data analysis methodology introduced by Peter
L. Hammer in 1986. LAD distinguishes itself from other classification and machine learning …

We introduce SIRUS (Stable and Interpretable RUle Set) for regression, a stable rule
learning algorithm, which takes the form of a short and simple list of rules. State-of-the-art …

This paper considers generalized linear models using rule-based features, also referred to
as rule ensembles, for regression and probabilistic classification. Rules facilitate model …

State-of-the-art learning algorithms, such as random forests or neural networks, are often
qualified as “black-boxes” because of the high number and complexity of operations …

Conventional rule learning algorithms aim at finding a set of simple rules, where each rule
covers as many examples as possible. In this paper, we argue that the rules found in this …

This study proposes a novel classification system integrating swarm and metaheuristic
intelligence, ie, a smart firefly algorithm (SFA), with a least squares support vector machine …

We propose an interpretable rule-based classification system based on ideas from Boolean
compressed sensing. We represent the problem of learning individual conjunctive clauses …

The wide adoption of machine learning approaches in the industry, government, medicine
and science has renewed the interest in interpretable machine learning: many decisions are …

In multi-label classification, where the evaluation of predictions is less straightforward than in
single-label classification, various meaningful, though different, loss functions have been …