Discovering comprehensible classification rules with a genetic algorithm
- 7 November 2002
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1, 805-810 vol.1
- https://doi.org/10.1109/cec.2000.870381
Abstract
Presents a classification algorithm based on genetic algorithms (GAs) that discovers comprehensible IF-THEN rules, in the spirit of data mining. The proposed GA has a flexible chromosome encoding, where each chromosome corresponds to a classification rule. Although the number of genes (the genotype) is fixed, the number of rule conditions (the phenotype) is variable. The GA also has specific mutation operators for this chromosome encoding. The algorithm was evaluated on two public-domain real-world data sets (in the medical domains of dermatology and breast cancer).Keywords
This publication has 9 references indexed in Scilit:
- Mining Very Large Databases with Parallel ProcessingPublished by Springer Science and Business Media LLC ,2000
- A Genetic Algorithm for Generalized Rule InductionPublished by Springer Science and Business Media LLC ,1999
- An Evolutionary Algorithm Using Multivariate Discretization for Decision Rule InductionLecture Notes in Computer Science, 1999
- Learning differential diagnosis of erythemato-squamous diseases using voting feature intervalsArtificial Intelligence in Medicine, 1998
- Discovery of decision rules from databases: An evolutionary approachPublished by Springer Science and Business Media LLC ,1998
- AN EVOLUTIONARY APPROACH TO SIMULATE COGNITIVE FEEDBACK LEARNING IN MEDICAL DOMAINPublished by World Scientific Pub Co Pte Ltd ,1997
- Using genetic algorithms for concept learningMachine Learning, 1993
- A Knowledge-Intensive Genetic Algorithm for Supervised LearningMachine Learning, 1993
- A theory and methodology of inductive learningArtificial Intelligence, 1983