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.
|Title of host publication||Proceedings of the International Conference on Tools with Artificial Intelligence|
|Number of pages||8|
|Publication status||Published - 1 Dec 1998|
|Event||Proceedings of the 1998 IEEE 10th International Conference on Tools with Artificial Intelligence - Taipei, China|
Duration: 10 Nov 1998 → 12 Nov 1998
|Conference||Proceedings of the 1998 IEEE 10th International Conference on Tools with Artificial Intelligence|
|Period||10/11/98 → 12/11/98|