First-Order Inductive Learner (FOIL) Algorithm

A First-Order Inductive Learner (FOIL) Algorithm is an inductive logic programming algorithm that uses a top-down greedy search based on a SEQUENTIAL-COVERING algorithm (directed by an information gain heuristic).

• AKA: Quinlan's FOIL Algorithm.
• Context:
  • It was initially developed by Quinlan (1990).
  • It is the precursor to FOCL Algorithm.
  • It can be implemented bi a FOIL System to solve a FOIL Task.
  • It can discover patterns in the data expressed as First-Order Rules.
  • It can also be implemented as a Rule Induction Algorithm by a Rule Induction System.
• Example(s)
  • LUCS-KDD FOIL Algorithm [1],
  • Wikipedia Foil Algorithm Pseudocode.
• Counter-Example(s):
  • CN2 Algorithm,
  • FINDBESTRULE Algorithm,
  • FURIA Algorithm,
  • LEM-based Algorithms,
  • PROGOL Algorithm,
  • RIPPER Algorithm,
  • Rulearner Algorithm,
  • RULEX Algorithm.
• See: ID3 Algorithm, Sequential-Covering Alogrithm, Top-Down Learning, Pattern Mining Algorithm, Decision Tree Induction Algorithm, Inductive Logic Programming, If-Then Rule, Firs-Order Logic Rule.

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References

2017

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/First-order_inductive_learner Retrieved:2017-11-12.
  • In machine learning, first-order inductive learner (FOIL) is a rule-based learning algorithm.

2004a

• (Melli, 2004) ⇒ Gabor Melli. (2004). “Scribe Notes on FOIL and Inverted Deduction.” In: Scribe Notes for the 2004 SFU course on Machine Learning (SFU CMPT-882 2004).

2004b

• (Coenen, 2004) ⇒ Frans Coenen (2004). Overview Of Foil Algorithm.
  • QUOTE: The FOIL algorithm (Quinlan and Cameron-Jones 1993) takes as input a (space separated) binary valued data set R and produces a set of CARs. The classifier, as generated by the LUCS-KDD FOIL algorithm described here, comprises a linked-list of rules ordered according to their Laplace accuracy (Clark and Boswell 1991).

1990

• (Quinlan, 1990) ⇒ J. Ross Quinlan. (1990). “Learning Logical Definitions from Examples.” In: Machine Learning, 5(3). doi:10.1007/BF00117105
  • QUOTE: This paper describes FOIL, a system that learns Horn clauses from data expressed as relations. FOIL is based on ideas that have proved effective in attribute-value learning systems, but extends them to a first-order formalism. This new system has been applied successfully to several tasks taken from the machine learning literature.

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