Integrating boosting and stochastic attribute selection committees for further improving the performance of decision tree learning

Zijian Zheng, Geoffrey I. Webb, Kai Ming Ting

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

5 Citations (Scopus)


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 paper, we propose a method for improving the performance of Boosting. This technique combines Boosting and SASC. It builds classifier committees by manipulating both the distribution of the training set and the set of attributes available during induction. In the synergy, SASC effectively increases the model diversity of Boosting. Experiments with a representative collection of natural domains show that, on average, the combined technique outperforms either Boosting or SASC alone in terms of reducing the error rate of decision tree learning.

Original languageEnglish
Title of host publicationProceedings of the International Conference on Tools with Artificial Intelligence
Number of pages8
Publication statusPublished - 1 Dec 1998
Externally publishedYes
EventInternational Conference on Tools with Artificial Intelligence 1998 - Taipei, China
Duration: 10 Nov 199812 Nov 1998
Conference number: 10th


ConferenceInternational Conference on Tools with Artificial Intelligence 1998
CityTaipei, China

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