FOIL: A Midterm Report

By J. Ross Quinlan, R. Mike Cameron-Jones. 1993

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Citations in the corpus

A Survey of Methods for Explaining Black Box Models (2018, neutral): “Indeed, following the basic idea of FOIL [94], CPAR does not generate a large set of candidates, as in associative classification, and applies a greedy approach for generating rules cд directly from training data.”
Inductive Logic Programming: Theory and Methods (1994, methodology): “Instead we would like the rules to predict an interval such as mesh(Obj;X) 7 X 9; connected(Obj;Obj1); : : : This kind of construction is not handled elegantly by existing systems (though LINUS [66] and more recently FOIL [103] can use TDIDT-extensions [101] to introduce tests such as X 9).”
CPAR: Classification based on Predictive Association Rules (2003, background): “FOIL (First Order Inductive Learner), proposed by Ross Quinlan in 1993 [9], is a greedy algorithm that learns rules to distinguish positive examples from negative ones.”
An Overview of Overfitting and its Solutions (2019, background): “Lots of previous works have proven the effectiveness of this strategy in inducing decision tree learning, such as CN2 [9], FOIL [10], and Fossil [11], Reduced Error Pruning (REP) [12], GROW [13] and J-pruning based on information-theoretic J-Measure [14].”
Bias reformulation for one-shot function induction (2014, background): “A somewhat smaller literature relates these two problems (eg [15]).”
Fast Effective Rule Induction (1995, background): “GrowRule repeatedly adds the condition that maximizes FOIL's information gain criterion until the rule covers no negative examples from the growing dataset [Quinlan 1990; Quinlan and Cameron-Jones 1993].”
A flexible learning system for wrapping tables and lists in HTML documents (2002, methodology): “There are several di.erences between this learning algorithm and FOIL.”
Neural networks for relational learning: an experimental comparison (2011, background): “1nn(dm) AB+C Ramon (2002) 88 RDBC AB+C Kirsten (2002) 82”
Random Walk Inference and Learning in A Large Scale Knowledge Base (2011, methodology): “NELL learns these Horn clause rules using a variant of the FOIL algorithm (Quinlan and Cameron-Jones, 1993), henceforth N-FOIL.”
Learning to construct knowledge bases from the World Wide Web (2000, methodology): “The learning algorithm that we use in this section is Quinlan's Foil algorithm [52,53].”
FURIA: an algorithm for unordered fuzzy rule induction (2009, methodology): “Rule growing: A new rule is learned on the growing data, using a propositional version of the FOIL algorithm (Quinlan, 1990; Quinlan and Cameron-Jones, 1993).”
A review of associative classification mining (2007, methodology): “For example, the IREP rule induction algorithm constructs the rules based on first-order inductive learner (FOIL)-gain measure (Quinlan & Cameron-Jones, 1993).”
Review and Comparison of Associative Classification Data Mining Approaches (2014, unknown): “Quinlan and Cameron-Jones [35] proposed the First Order Inductive Learner (FOIL) this learning strategy produces rules for each class cases in the training data.”
Link-Based Classification (2003, methodology): “Using these statistical features and a relational rule learner based on FOIL (Quinlan & Cameron-Jones, 1993), they propose a combined model for text classification.”
Computer-aided auditing of prescription drug claims (2013, methodology): “Next, we will contrast our rule generation heuristic with that used in classical algorithms like FOIL and RIPPER [6, 19].”