Foundations of Rule Learning

By Johannes Fürnkranz, Dragan Gamberger, Nada Lavrac. 2012

373 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

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Citations in the corpus

Boolean Decision Rules via Column Generation (2018, background): “Predominant practical approaches include a covering or separate-and-conquer strategy ((13; 12; 14; 23; 25; 36), see also the survey (27)) of learning rules one by one and removing “covered” examples, a bottom-up strategy of combining more speciﬁc rules into more general ones (41; 19; 37), and…”
On expressiveness and uncertainty awareness in rule-based classification for data streams (2017, background): “Also, the authors of [32] discuss that 200”
Materials informatics (2018, background): “Forexample,comparingwiththoseblack-box learning models (i.e. artiﬁcial neural network, support vector machine, etc.), rule learning (Fürnkranz et al. 2012) (i.e. RIP-PER) is a white-box model that can learn set of rules from the training data.”
Tunneling Neural Perception and Logic Reasoning through Abductive Learning (2018, background): “In this branch of AI research, a series of important steps has been achieved, e.g., automatic theorem proving [33, 5], propositional rule learning [9], expert systems [18], automated planning [8] and inductive logic programming [31], and so on.”
Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges (2020, background): “Rule-based ML, which covers decision rules and decision trees, has been an active research area since the middle of the 20th century [35].”
Learning sparse two-level boolean rules (2016, background): “In fact, if the input features are binary and we include all negations, then two-level rules can represent any Boolean function of the input features [5]; moreover, continuous valued features may be incorporated by converting them to binary with threshold comparisons.”