FOIL

Induction of logic programs: FOIL and related systems. FOIL is a first-order learning system that uses information in a collection of relations to construct theories expressed in a dialect of Prolog. This paper provides an overview of the principal ideas and methods used in the current version of the system, including two recent additions. We present examples of tasks tackled by FOIL and of systems that adapt and extend its approach.


References in zbMATH (referenced in 31 articles , 1 standard article )

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  1. Bénard, Clément; Biau, Gérard; Da Veiga, Sébastien; Scornet, Erwan: SIRUS: stable and interpretable RUle set for classification (2021)
  2. Muggleton, Stephen H.; Schmid, Ute; Zeller, Christina; Tamaddoni-Nezhad, Alireza; Besold, Tarek: Ultra-strong machine learning: comprehensibility of programs learned with ILP (2018)
  3. Bellodi, Elena; Riguzzi, Fabrizio: Structure learning of probabilistic logic programs by searching the clause space (2015)
  4. Nandhini, M.; Sivanandam, S. N.: An improved predictive association rule based classifier using gain ratio and T-test for health care data diagnosis (2015) ioport
  5. Angiulli, Fabrizio; Fassetti, Fabio: Exploiting domain knowledge to detect outliers (2014)
  6. Zang, Liang-Jun; Cao, Cong; Cao, Ya-Nan; Wu, Yu-Ming; Cao, Cun-Gen: A survey of commonsense knowledge acquisition (2013)
  7. Zheng, Yifeng; Huang, Zaixiang; He, Tianzhong: Classification based on both attribute value weight and tuple weight under the cloud computing (2013) ioport
  8. Muggleton, Stephen; De Raedt, Luc; Poole, David; Bratko, Ivan; Flach, Peter: ILP turns 20. Biography and future challenges (2012)
  9. Błaszczyński, Jerzy; Słowiński, Roman; Szeląg, Marcin: Sequential covering rule induction algorithm for variable consistency rough set approaches (2011) ioport
  10. Lippi, Marco; Jaeger, Manfred; Frasconi, Paolo; Passerini, Andrea: Relational information gain (2011)
  11. Uwents, Werner; Monfardini, Gabriele; Blockeel, Hendrik; Gori, Marco; Scarselli, Franco: Neural networks for relational learning: an experimental comparison (2011) ioport
  12. Vreeken, Jilles; Van Leeuwen, Matthijs; Siebes, Arno: Krimp: mining itemsets that compress (2011)
  13. Hühn, Jens; Hüllermeier, Eyke: FURIA: an algorithm for unordered fuzzy rule induction (2009) ioport
  14. Kitzelmann, Emanuel: Analytical inductive functional programming (2009)
  15. Fonseca, Nuno A.; Srinivasan, Ashwin; Silva, Fernando; Camacho, Rui: Parallel ILP for distributed-memory architectures (2008) ioport
  16. Guo, Hongyu; Viktor, Herna L.: Multirelational classification: a multiple view approach (2008) ioport
  17. Bădică, Costin; Bădică, Amelia; Popescu, Elvira; Abraham, Ajith: L-wrappers: concepts, properties and construction (2007) ioport
  18. Kotsiantis, S. B.; Zaharakis, I. D.; Pintelas, P. E.: Machine learning: a review of classification and combining techniques (2007) ioport
  19. Ohshima, Muneaki; Zhong, Ning; Yao, YiYu; Liu, Chunnian: Relational peculiarity-oriented mining (2007) ioport
  20. Yin, Xiaoxin; Han, Jiawei; Yu, Philip S.: Crossclus: user-guided multi-relational clustering (2007)

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