An Empirical Comparison of Genetic and Decision-Tree Classifiers
作者： J.R. QUINLAN
DOI: 10.1016/B978-0-934613-64-4.50019-0
关键词: Decision tree 、 ID3 algorithm 、 Property (programming) 、 Data mining 、 Task (project management) 、 Decision tree learning 、 Incremental decision tree 、 Machine learning 、 Artificial intelligence 、 Empirical comparison 、 Computer science 、 Alternating decision tree
摘要: Abstract Wilson has reported results obtained by a genetic learning system Boole on small, abstract task. This task is shown to have property that complicates its analysis top-down decision tree methods. Nevertheless, experiments with methods accurate classifiers can be from comparatively small sets of training examples. Finally, conversion the trees production rules led significant improvement in classification accuracy for this
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