MILCS in protein structure prediction with default hierarchies

ABSTRACT

This paper studies the performance of a newly developed supervised Michigan-style learning classifier system (LCS), called MILCS, on protein structure prediction problems and our observation of its default hierarchies (DHs). We present experimental results, and contrast them to results from other machine learning systems, named XCS, UCS, GAssist, BioHEL, C4.5 and Naïve Bayes. We use our technique for visualizing explanatory power of the resulting rule sets and their hierarchical structure. Final comments include future directions for this research, including investigations in neural networks and other systems.

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INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Induction

General Terms:
Algorithms

Keywords:
evolutionary computation, explanatory power, information theory, learning classifier systems, machine learning, mutual information, protein structure prediction, supervised learning