kFOIL: learning simple relational kernels. A novel and simple combination of inductive logic programming with kernel methods is presented. The kFOIL algorithm integrates the well-known inductive logic programming system FOIL with kernel methods. The feature space is constructed by leveraging FOIL search for a set of relevant clauses. The search is driven by the performance obtained by a support vector machine based on the resulting kernel. In this way, kFOIL implements a dynamic propositionalization approach. Both classification and regression tasks can be naturally handled. Experiments in applying kFOIL to well-known benchmarks in chemoinformatics show the promise of the approach.

References in zbMATH (referenced in 12 articles )

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  1. Shakerin, Farhad; Gupta, Gopal: White-box induction from SVM models: explainable AI with logic programming (2020)
  2. Michelioudakis, Evangelos; Artikis, Alexander; Paliouras, Georgios: Semi-supervised online structure learning for composite event recognition (2019)
  3. Zhao, Jinwei; Hei, Xinhong; Shi, Zhenghao; Dong, Longlei; Liu, Yu; Yan, Ruiping; Li, Xiuxiu: Regression learning based on incomplete relationships between attributes (2018)
  4. Diligenti, Michelangelo; Gori, Marco; Saccà, Claudio: Semantic-based regularization for learning and inference (2017)
  5. Orsini, Francesco; Frasconi, Paolo; De Raedt, Luc: kProbLog: an algebraic Prolog for machine learning (2017)
  6. Orsini, Francesco; Frasconi, Paolo; De Raedt, Luc: kProbLog: an algebraic Prolog for kernel programming (2016)
  7. Diligenti, Michelangelo; Gori, Marco; Maggini, Marco; Rigutini, Leonardo: Bridging logic and kernel machines (2012)
  8. Kuželka, Ondřej; Železný, Filip: Block-wise construction of tree-like relational features with monotone reducibility and redundancy (2011)
  9. Landwehr, Niels; Passerini, Andrea; De Raedt, Luc; Frasconi, Paolo: Fast learning of relational kernels (2010)
  10. Bringmann, Björn; Zimmermann, Albrecht: One in a million: picking the right patterns (2009) ioport
  11. Specia, Lucia; Srinivasan, Ashwin; Joshi, Sachindra; Ramakrishnan, Ganesh; Nunes, Maria Das Graças Volpe: An investigation into feature construction to assist word sense disambiguation (2009)
  12. Rückert, Ulrich; Kramer, Stefan: Margin-based first-order rule learning (2007)