Integrating Boosting and stochastic attribute selection committees for further improving the performance of decision tree learning (1998) [2 citations — 1 self]

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by Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting

Proceedings of the 10th IEEE International Conference on Tools with Artificial Intelligence. IEEE Computer

http://www3.cm.deakin.edu.au/~zijian/Papers/sascb-ictai98.ps.gz

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@INPROCEEDINGS{Zheng98integratingboosting,
    author = {Zijian Zheng and Geoffrey I. Webb and Kai Ming Ting},
    title = {Integrating Boosting and stochastic attribute selection committees for further improving the performance of decision tree learning},
    booktitle = {Proceedings of the 10th IEEE International Conference on Tools with Artificial Intelligence. IEEE Computer},
    year = {1998},
    pages = {216--223},
    publisher = {Society Press}
}

Abstract:

Techniques for constructing classifier committees including Boosting and Bagging have demonstrated great success, especially Boosting for decision tree learning. This type of technique generates several classifiers to form a committee by repeated application of a single base learning algorithm. The committee members vote to decide the final classification. Boosting and Bagging create different classifiers by modifying the distribution of the training set. SASC (Stochastic Attribute Selection Committees) uses an alternative approach to generating classifier committees by stochastic manipulation of the set of attributes considered at each node during tree induction, but keeping the distribution of the training set unchanged. In this

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