This paper reports the implementation of DRAPH-GP an extension of the decision graph algorithm DGRAPH-OW using the AdaBoost algorithm. This algorithm, which we call 1Stage Boosting, is shown to improve the accuracy of decision graphs, along with another technique which we combine with AdaBoost and call 2-Stage Boosting which shows greater improvement. Empirical tests demonstrate that both 1-Sta...

Boosting is a popular way to derive powerful learners from simpler hypothesis classes. Following previous work (Mason et al., 1999; Friedman, 2000) on general boosting frameworks, we analyze gradient-based descent algorithms for boosting with respect to any convex objective and introduce a new measure of weak learner performance into this setting which generalizes existing work. We present the ...

This paper investigates boosting naive Bayesian classiica-tion. It rst shows that boosting cannot improve the accuracy of the naive Bayesian classiier on average in a set of natural domains. By analyzing the reasons of boosting's failures, we propose to introduce tree structures into naive Bayesian classiication to improve the performance of boosting when working with naive Bayesian classiicati...

Breiman’s bagging and Freund and Schapire’s boosting are recent methods for improving the predictive power of classifier learning systems. Both form a set of classifiers that are combined by voting, bagging by generating replicated bootstrap samples of the data, and boosting by adjusting the weights of training instances. This paper reports results of applying both techniques to a system that l...

The firing evoked by injection of simulated barrages of EPSCs into the proximal dendrite of layer 5 pyramidal neurons is greater than when comparable inputs are injected into the soma. This boosting is mediated by dendritic Na+ conductances. However, the presence of other active conductances in the dendrites, some of which are nonuniformly distributed, suggests that the degree of boosting may d...

In this paper we present an empirical comparison of algorithm AdaBoost with its modification called MadaBoost suitable for the boosting by filtering framework. In the boosting by filtering one obtains an unweighted sample at each stage that is randomly drawn from the current modified distribution in contrast with the boosting by subsampling where one uses a weighted sample at each stage. A boos...

Although boosting methods have become an extremely important classification method, there has been little attention paid to boosting with asymmetric losses. In this paper we take a gradient descent view of boosting in order to motivate a new boosting variant called BiBoost which treats the two classes differently. This variant is likely to perform well when there is a different cost for false p...

Boosting is known as a gradient descent algorithm over loss functions. It is often pointed out that the typical boosting algorithm, Adaboost, is highly affected by outliers. In this letter, loss functions for robust boosting are studied. Based on the concept of robust statistics, we propose a transformation of loss functions that makes boosting algorithms robust against extreme outliers. Next, ...

Boosting algorithms have been shown to perform well on many realworld problems, although they sometimes tend to overfit in noisy situations. While excellent finite sample bounds are known, it has not been clear whether boosting is statistically consistent, implying asymptotic convergence to the optimal classification rule. Recent work has provided sufficient conditions for the consistency of bo...

Breiman's bagging and Freund and Schapire's boosting are recent methods for improving the predictive power of classiier learning systems. Both form a set of classiiers that are combined by voting, bagging by generating replicated boot-strap samples of the data, and boosting by adjusting the weights of training instances. This paper reports results of applying both techniques to a system that le...