Videos tagged with Ensemble Methods

Many feature selection algorithms are limited in that they attempt to identify relevant feature subsets by examining the features individually. This paper introduces a technique for determining feature relevance using the average information gain achieved during the construction of decision tree ensembles. The technique introduces a node complexity measure and a statistical method for updating ...

We propose a thresholded ensemble model for ordinal regression problems. The model consists of a weighted ensemble of confidence functions and an ordered vector of thresholds. Using such a model, we could theoretically and algorithmically reduce ordinal regression problems to binary classification problems in the area of ensemble learning. Based on the reduction, we derive novel large-margin bo...

We consider the risk, or probability of error, of the classifier produced by AdaBoost, and in particular the stopping strategy to be used to ensure universal consistency. (A classification method is universally consistent if the risk of the classifiers it produces approaches the Bayes risk---the minimal risk---as the sample size grows.) Several related algorithms---regularized versions of AdaBo...

I will give an overview of recent developments in boosting, focusing on three papers which take very different approaches towards making boosting more efficient and effective. Boosters iteratively choose base classifiers via a weak learner and then update a distribution over training examples. Roughly, the three papers show progress on the three issues implicit in this one-sentence description ...