A comparison of stacking with meta decision trees to other combining methods
نویسنده
چکیده
Meta decision trees (MDTs) are a method for combining multiple classifiers. We present an integration of the algorithm MLC4.5 for learning MDTs into the Weka data mining suite. We compare classifier ensembles combined with MDTs to bagged and boosted decision trees, and to classifier ensembles combined with other methods: voting, grading, multi-scheme and stacking with multi-response linear regression.
منابع مشابه
A comparison of stacking with MDTs to bagging, boosting, and other stacking methods
In this paper, we present an integration of the algorithm MLC4.5 for learning meta decision trees (MDTs) into the Weka data mining suite. MDTs are a method for combining multiple classifiers. Instead of giving a prediction, MDT leaves specify which classifier should be used to obtain a prediction. The algorithm is based on the C4.5 algorithm for learning ordinary decision trees. An extensive pe...
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Meta decision trees (MDTs) are a method for combining multiple classifiers. We present an integration of the algorithm MLC4.5 for learning MDTs into the Weka data mining suite. We compare classifier ensembles combined with MDTs to bagged and boosted decision trees, and to classifier ensembles combined with other methods: voting and stacking with three different meta-level classifiers (ordinary ...
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تاریخ انتشار 2001