{19 () Improving Bagging Performance by Increasing Decision Tree Diversity
نویسنده
چکیده
Ensembles of decision trees often exhibit greater predictive accuracy than single trees alone. Bagging and boosting are two standard ways of generating and combining multiple trees. Boosting has been empirically determined to be the more eeective of the two, and it has recently been proposed that this may be because it produces more diverse trees than bagging. This paper reports empirical ndings that strongly support this hypothesis. We enforce greater decision tree diversity in bagging by a simple modiication of the underlying decision tree learner that utilizes randomly-generated decision stumps of predeened depth as the starting point for tree induction. The modiied procedure yields very competitive results while still retaining one of the attractive properties of bagging: all iterations are independent. Additionally, we also investigate a possible integration of bagging and boosting. All these ensemble-generating procedures are compared empirically on various domains.
منابع مشابه
Integrating boosting and stochastic attribute selection committees for further improving the performance of decision tree learning
Techniques for constructing classiier committees including Boosting and Bagging have demonstrated great success, especially Boosting for decision tree learning. This type of technique generates several classiiers to form a committee by repeated application of a single base learning algorithm. The committee members vote to decide the nal classiication. Boosting and Bagging create diierent classi...
متن کاملImproving reservoir rock classification in heterogeneous carbonates using boosting and bagging strategies: A case study of early Triassic carbonates of coastal Fars, south Iran
An accurate reservoir characterization is a crucial task for the development of quantitative geological models and reservoir simulation. In the present research work, a novel view is presented on the reservoir characterization using the advantages of thin section image analysis and intelligent classification algorithms. The proposed methodology comprises three main steps. First, four classes of...
متن کاملStochastic Attribute Selection Committees withMultiple Boosting : Learning More
Classiier learning is a key technique for KDD. Approaches to learning classiier committees, including Boosting, Bagging, Sasc, and SascB, have demonstrated great success in increasing the prediction accuracy of decision trees. Boosting and Bagging create diierent classiiers by modifying the distribution of the training set. Sasc adopts a diierent method. It generates committees by stochastic ma...
متن کاملEnsemble Learning with Decision Tree for Remote Sensing Classification
In recent years, a number of works proposing the combination of multiple classifiers to produce a single classification have been reported in remote sensing literature. The resulting classifier, referred to as an ensemble classifier, is generally found to be more accurate than any of the individual classifiers making up the ensemble. As accuracy is the primary concern, much of the research in t...
متن کاملDistributed learning with bagging - like performance 3
10 Bagging forms a committee of classifiers by bootstrap aggregation of training sets from a pool of training data. A 11 simple alternative to bagging is to partition the data into disjoint subsets. Experiments with decision tree and neural 12 network classifiers on various datasets show that, given the same size partitions and bags, disjoint partitions result in 13 performance equivalent to, o...
متن کامل