Ensemble methods in machine learning
TG Dietterich - International workshop on multiple classifier systems, 2000 - Springer
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 …
classify new data points by taking a (weighted) vote of their predictions. The original …
Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and EI George, and a rejoinder by the authors
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 …
model from some class of models and then proceed as if the selected model had generated …
[HTML][HTML] Estimation of daily PM10 and PM2. 5 concentrations in Italy, 2013–2015, using a spatiotemporal land-use random-forest model
M Stafoggia, T Bellander, S Bucci, M Davoli… - Environment …, 2019 - Elsevier
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 …
demonstrated adverse effects from both short-term and long-term exposure. Most of the …
Bagging predictors
L Breiman - Machine learning, 1996 - Springer
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 …
these to get an aggregated predictor. The aggregation averages over the versions when …
Wrappers for feature subset selection
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 …
selecting a relevant subset of features upon which to focus its attention, while ignoring the …
The random subspace method for constructing decision forests
TK Ho - IEEE transactions on pattern analysis and machine …, 1998 - ieeexplore.ieee.org
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 …
of tree sizes. The dilemma between overfitting and achieving maximum accuracy is seldom …
A Bayesian method for the induction of probabilistic networks from data
GF Cooper, E Herskovits - Machine learning, 1992 - Springer
This paper presents a Bayesian method for constructing probabilistic networks from
databases. In particular, we focus on constructing Bayesian belief networks. Potential …
databases. In particular, we focus on constructing Bayesian belief networks. Potential …
An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
E Bauer, R Kohavi - Machine learning, 1999 - Springer
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 …
shown to be very successful in improving the accuracy of certain classifiers for artificial and …
Automatic construction of decision trees from data: A multi-disciplinary survey
SK Murthy - Data mining and knowledge discovery, 1998 - Springer
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 …
generalization of data. Work on constructing decision trees from data exists in multiple …
Machine-learning research
TG Dietterich - AI magazine, 1997 - ojs.aaai.org
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 …
This article summarizes four of these directions and discusses some current open problems …