Ensemble methods are learning algorithms that construct a set of classifiers and then
classify new data points by taking a (weighted) vote of their predictions. The original …

Standard statistical practice ignores model uncertainty. Data analysts typically select a
model from some class of models and then proceed as if the selected model had generated …

Particulate matter (PM) air pollution is one of the major causes of death worldwide, with
demonstrated adverse effects from both short-term and long-term exposure. Most of the …

Bagging predictors is a method for generating multiple versions of a predictor and using
these to get an aggregated predictor. The aggregation averages over the versions when …

In the feature subset selection problem, a learning algorithm is faced with the problem of
selecting a relevant subset of features upon which to focus its attention, while ignoring the …

Much of previous attention on decision trees focuses on the splitting criteria and optimization
of tree sizes. The dilemma between overfitting and achieving maximum accuracy is seldom …

This paper presents a Bayesian method for constructing probabilistic networks from
databases. In particular, we focus on constructing Bayesian belief networks. Potential …

Methods for voting classification algorithms, such as Bagging and AdaBoost, have been
shown to be very successful in improving the accuracy of certain classifiers for artificial and …

Decision trees have proved to be valuable tools for the description, classification and
generalization of data. Work on constructing decision trees from data exists in multiple …

Abstract Machine-learning research has been making great progress in many directions.
This article summarizes four of these directions and discusses some current open problems …