Machine Learning

, Volume 26, Issue 2, pp 147–176

First Order Regression

  • Aram Karalič
  • Ivan Bratko

DOI: 10.1023/A:1007365207130

Cite this article as:
Karalič, A. & Bratko, I. Machine Learning (1997) 26: 147. doi:10.1023/A:1007365207130


We present a new approach, called First Order Regression (FOR), to handling numerical information in Inductive Logic Programming (ILP). FOR is a combination of ILP and numerical regression. First-order logic descriptions are induced to carve out those subspaces that are amenable to numerical regression among real-valued variables. The program FORS is an implementation of this idea, where numerical regression is focused on a distinguished continuous argument of the target predicate. We show that this can be viewed as a generalisation of the usual ILP problem. Applications of FORS on several real-world data sets are described: the prediction of mutagenicity of chemicals, the modelling of liquid dynamics in a surge tank, predicting the roughness in steel grinding, finite element mesh design, and operator's skill reconstruction in electric discharge machining. A comparison of FORS' performance with previous results in these domains indicates that FORS is an effective tool for ILP applications that involve numerical data.

machine learninginductive logic programmingregressionreal-valued variablesfirst-order logicapplications of machine learning
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Copyright information

© Kluwer Academic Publishers 1997

Authors and Affiliations

  • Aram Karalič
    • 1
  • Ivan Bratko
    • 2
  1. 1.Jožef Stefan InstituteLjubljanaSlovenia
  2. 2.Faculty of Electrical Engineering and Computer ScienceUniversity of Ljubljana, and Jozef Stefan InstituteLjubljanaSlovenia