Embedding Decision Trees and Random Forests in Constraint Programming

  • Alessio BonfiettiEmail author
  • Michele Lombardi
  • Michela Milano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9075)


In past papers, we have introduced Empirical Model Learning (EML) as a method to enable Combinatorial Optimization on real world systems that are impervious to classical modeling approaches. The core idea in EML consists in embedding a Machine Learning model in a traditional combinatorial model. So far, the method has been demonstrated by using Neural Networks and Constraint Programming (CP). In this paper we add one more technique to the EML arsenal, by devising methods to embed Decision Trees (DTs) in CP. In particular, we propose three approaches: 1) a simple encoding based on meta-constraints; 2) a method using attribute discretization and a global table constraint; 3) an approach based on converting a DT into a Multi-valued Decision Diagram, which is then fed to an mdd constraint. We finally show how to embed in CP a Random Forest, a powerful type of ensemble classifier based on DTs. The proposed methods are compared in an experimental evaluation, highlighting their strengths and their weaknesses.


Random Forest Constraint Program Numeric Attribute Global Constraint Machine Learn Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alessio Bonfietti
    • 1
    Email author
  • Michele Lombardi
    • 1
  • Michela Milano
    • 1
  1. 1.DISIUniversity of BolognaBolognaItaly

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