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Bandit-Based Structure Learning for Bayesian Network Classifiers

  • Sepehr Eghbali
  • Mohammad Hassan Zokaei Ashtiani
  • Majid Nili Ahmadabadi
  • Babak Nadjar Araabi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7664)

Abstract

In this work, we tackle the problem of structure learning for Bayesian network classifiers (BNC). Searching for an appropriate structure is a challenging task since the number of possible structures grows exponentially with the number of attributes. We formulate this search problem as a large Markov Decision Process (MDP). This allows us to tackle the problem using sequential decision making methods. Furthermore, we devise a Monte Carlo tree search algorithm to find a tractable solution for the MDP. The use of bandit-based action selection strategy enables us to have a systematic way of guiding the search, making the search in the large space of unrestricted structures tractable. The results of classification on different datasets show that the use of this method can significantly boost the performance of structure learning for BNCs.

Keywords

Bayesian network classifier Structure learning Reinforcement learning Bandit methods Monte Carlo tree search 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Sepehr Eghbali
    • 1
  • Mohammad Hassan Zokaei Ashtiani
    • 1
  • Majid Nili Ahmadabadi
    • 1
    • 2
  • Babak Nadjar Araabi
    • 1
    • 2
  1. 1.Cognitive Robotics Lab, Control and Intelligent Processing Center of Excellence, School of ECE., College of Eng.Univ. of TehranIran
  2. 2.School of Cognitive SciencesInstitute for Research in Fundamental Sciences, IPMIran

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