Threshold rules decision list
نویسنده
چکیده
Understanding data is one of most important problems. Popular crisp logic rules are easy to understand and compare, however for some datasets the number of extracted rules is very large, what affect reduction of generalization and makes the system less transparent. Another solution are fuzzy logic rules, which are much more flexible, however they don’t support symbolic and nominal attributes. Alternative systems for rules extraction base on prototype rules, this type of rules drives from similarity base learning. Presented threshold rules algorithm extracts form data small number of ordered rules, which are very accurate. Numerical experiments on real data show the usefulness of such approach as an alternative to neurofuzzy models.
منابع مشابه
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