Applying Genetic and Symbolic Learning Algorithms to Extract Rules from Artificial Neural Networks

No abstract is available for this paper.

• Publication year

  2004

• Venue

  Mexican International Conference on Artificial Intelligence

• Publication date

  2004-04-26

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1007/978-3-540-24694-7_86
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• The CN2 induction algorithm

  2004influential reference
• Extraction of knowledge from Artificial Neural Networks using Symbolic Machine Learning Systems and Genetic Algorithm

  2003cited by this paper
• Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms

  1998cited by this paper
• Extracting comprehensible models from trained neural networks

  1996cited by this paper
• Survey and critique of techniques for extracting rules from trained artificial neural networks

  1995cited by this paper
• C4.5: Programs for Machine Learning

  1992influential reference
• Computer Systems That Learn

  1990cited by this paper
• Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations

  1986cited by this paper
• Learning internal representations by error propagation

  1986cited by this paper
• Programs for Machine Learning. Part I

  1962cited by this paper

• GenFastLAS: Scaling up the FastLAS system to large feature spaces, and application to Symbolic Music Genre Classification

  2022cites this paper
• Genetic generation of fuzzy knowledge bases: new perspectives

  2012cites this paper
• A hybrid approach to learn with imbalanced classes using evolutionary algorithms

  2011cites this paper
• Evolutionary Computation in Brazil: A Review of the Literature in Two Databases

  2010cites this paper
• A Study of the Influence of Rule Measures in Classifiers Induced by Evolutionary Algorithms

  2010cites this paper
• Avaliac ¸ ˜ ao de uma Abordagem Hpara Aprender com Classes Desbalanceadas: Resultados Experimentais com o Indutor CN2

  2009cites this paper
• Evolutionary tuning of SVM parameter values in multiclass problems

  2008cites this paper
• A Comparison of Methods for Rule Subset Selection Applied to Associative Classification

  2006cites this paper