Incremental Reduced Error Pruning
作者： Johannes Fürnkranz , Gerhard Widmer
DOI: 10.1016/B978-1-55860-335-6.50017-9
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摘要: Abstract This paper outlines some problems that may occur with Reduced Error Pruning in relational learning algorithms, most notably efficiency. Thereafter a new method, Incremental Pruning, is proposed attempts to address all of these problems. Experiments show many noisy domains this method much more efficient than alternative along slight gain accuracy. However, the experiments as well use algorithm cannot be recommended for which require very specific concept description.
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