Location of Repository

The MML Evolution of Classification Graphs

By Julian Neil and Kevin B. Korb

Abstract

. Minimum encoding induction (MML and MDL) is well developed theoretically and is currently being employed in two central areas of investigation in machine learning---namely, classification learning and the learning of causal networks. MML and MDL offer important tools for the evaluation of models, but offer little direct help in the problem of how to conduct the search through the model space. Here we combine MML with one of the more powerful search techniques available to machine learning: genetic algorithms (GAs). We develop a genetic algorithm to search the space of classification (decision) graphs using an MML-based fitness criterion and establish its effectiveness across a range of test cases from the UC Irvine machine learning archive. Keywords : Machine learning, MML induction, classification learning, MML genetic algorithms, concept formation, decision trees, C4.5. 1 Introduction The use of information-theoretic measures to evaluate models of data by ranking them according t..

Topics: Machine learning, MML induction, classification learning, MML genetic algorithms, concept formation, decision trees, C4.5
Year: 1996
OAI identifier: oai:CiteSeerX.psu:10.1.1.36.1970
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.csse.monash.edu.au/... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.