An Evaluation of Discretization Methods for Learning Rules from Biomedical Datasets.
作者： Shyam Visweswaran , Jonathan L. Lustgarten , Himanshu Grover , Vanathi Gopalakrishnan
DOI:
关键词: Knowledge extraction 、 Machine learning 、 Bayesian probability 、 Discretization 、 Selection (linguistics) 、 Artificial intelligence 、 Domain (software engineering) 、 Minimum description length 、 Computer science 、 Standard technique 、 Rule sets
摘要: Rule learning has the major advantage of understandability by human experts when performing knowledge discovery within biomedical domain. Many rule algorithms require discrete data in order to learn IF-THEN sets. This requirement makes selection a discretization technique an important step learning. We compare performance one standard technique, Fayyad and Irani’s Minimum Description Length Principle Criterion, which is defacto method many machine packages, that new Efficient Bayesian Discretization (EBD) show EBD leads significant gains especially as complexity learner increases.
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