Scaling regression to large datasets is a common problem in many application areas. We propose a two step approach to scaling regression to large datasets. Using a regression tree (CART) to segment the large dataset constitutes the first step of this approach. The second step of this approach is to develop a suitable regression model for each segment. Since segment sizes are not very large, we ...

In this paper, we tackle the problem of feature-efficient prediction: classification using a limited number of features per test example. We show that modifying an ensemble classifier such as AdaBoost, by sampling hypotheses from its final weighted predictor, is well-suited for this task. We further consider an extension of this problem, where the costs of examining the various features can dif...

We consider the problem of incrementally learning models from relational data. Most existing learning methods for statistical relational models use batch learning, which becomes computationally expensive and eventually infeasible for large datasets. The majority of the previous work in relational incremental learning assumes the model’s structure is given and only the model’s parameters needed ...

A new method for ensemble generation is presented. It is based on grouping the attributes in di erent subgroups, and to apply, for each group, an axis rotation, using Principal Component Analysis. If the used method for the induction of the classi ers is not invariant to rotations in the data set, the generated classi er can be very different. Hence, once of the objectives aimed when generating...

One of the most promising approaches for complex technical systems analysis employs ensemble methods of classification. Ensemble methods enable to build a reliable decision rules for feature space classification in the presence of many possible states of the system. In this paper, novel techniques based on decision trees are used for evaluation of the reliability of the regime of electric power...

The “minimum margin” of an ensemble classifier on a given training set is, roughly speaking, the smallest vote it gives to any correct training label. Recent work has shown that the Adaboost algorithm is particularly effective at producing ensembles with large minimum margins, and theory suggests that this may account for its success at reducing generalization error. We note, however, that the ...

We propose to train an ensemble with the help of a reservoir in which the learning algorithm can store a limited number of samples. This novel approach lies in the area between offline and online ensemble approaches and can be seen either as a restriction of the former or an enhancement of the latter. We identify some basic strategies that can be used to populate this reservoir and present our ...

This paper investigates the use of ensemble of predictors in order to improve the performance of spatial prediction methods. Support vector regression (SVR), a popular method from the field of statistical machine learning, is used. Several instances of SVR are combined using different data sampling schemes (bagging and boosting). Bagging shows good performance, and proves to be more computation...

Machine learning has become a powerful approach in practical applications, such as decision making, sentiment analysis and ontology engineering. To improve the overall performance in machine learning tasks, ensemble learning has become increasingly popular by combining different learning algorithms or models. Popular approaches of ensemble learning include Bagging and Boosting, which involve vo...

Bagging ensemble selection (BES) is a relatively new ensemble learning strategy. The strategy can be seen as an ensemble of the ensemble selection from libraries of models (ES) strategy. Previous experimental results on binary classification problems have shown that using random trees as base classifiers, BES-OOB (the most successful variant of BES) is competitive with (and in many cases, super...