Abstract: Classification, using the decision tree algorithm, is a widely studied problem in data streams. The challenge is when to split a decision node into multiple leaves. Concentration inequalities, that exploit variance information such as Bernstein’s and Bennett’s inequalities, are often substantially strict as compared with Hoeffding’s bound which disregards variance. Many machine learning algorithms for stream classification such as very fast decision tree (VFDT) learner, AdaBoost and support vector machines (SVMs), use the Hoeffding’s bound as a performance guarantee. In this paper, we propose a new algorithm based on the recently proposed empirical Bernstein’s bound to achieve a better probabilistic bound on the accuracy of the decision tree. Experimental results on four synthetic and two real world data sets demonstrate the performance gain of our proposed technique.

Key words:

Hoeffding and Bernstein’s bounds| data stream classification| decision tree| anytime algorithm

摘要： Classification, using the decision tree algorithm, is a widely studied problem in data streams. The challenge is when to split a decision node into multiple leaves. Concentration inequalities, that exploit variance information such as Bernstein’s and Bennett’s inequalities, are often substantially strict as compared with Hoeffding’s bound which disregards variance. Many machine learning algorithms for stream classification such as very fast decision tree (VFDT) learner, AdaBoost and support vector machines (SVMs), use the Hoeffding’s bound as a performance guarantee. In this paper, we propose a new algorithm based on the recently proposed empirical Bernstein’s bound to achieve a better probabilistic bound on the accuracy of the decision tree. Experimental results on four synthetic and two real world data sets demonstrate the performance gain of our proposed technique.

关键词:

Hoeffding and Bernstein’s bounds| data stream classification| decision tree| anytime algorithm