Learning effective classifiers with \(Z\)-value measure based on genetic programming

DOI10.1016/j.patcog.2004.03.016zbMath1072.68550OpenAlexW2129775304MaRDI QIDQ1887801

Jung-Yi Lin, Wei-Pang Yang, Been-Chian Chien

Publication date: 22 November 2004

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2004.03.016

zbMATH Keywords

ClassificationMachine learningKnowledge discoveryGenetic programming\(Z\)-value measure

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items

Designing a classifier by a layered multi-population genetic programming approach

Uses Software

• UCI-ml

Cites Work

• Bayesian network classifiers
• A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms
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