Multiclass Alternating Decision Trees
作者： Geoffrey Holmes , Bernhard Pfahringer , Richard Kirkby , Eibe Frank , Mark Hall
关键词: Machine learning 、 Boosting (machine learning) 、 Multiclass classification 、 LogitBoost 、 Artificial intelligence 、 Binary classification 、 Computer science 、 Decision tree 、 Probability learning 、 Alternating decision tree
摘要: The alternating decision tree (ADTree) is a successful classification technique that combines trees with the predictive accuracy of boosting into set interpretable rules. original formulation induction algorithm restricted attention to binary problems. This paper empirically evaluates several wrapper methods for extending multiclass case by splitting problem two-class Seeking more natural solution we then adapt LogitBoost and AdaBoost.MH procedures induce directly. Experimental results confirm these are comparable based on ADTree in accuracy, while inducing much smaller trees.
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