9,673 research outputs found
Popular Ensemble Methods: An Empirical Study
An ensemble consists of a set of individually trained classifiers (such as
neural networks or decision trees) whose predictions are combined when
classifying novel instances. Previous research has shown that an ensemble is
often more accurate than any of the single classifiers in the ensemble. Bagging
(Breiman, 1996c) and Boosting (Freund and Shapire, 1996; Shapire, 1990) are two
relatively new but popular methods for producing ensembles. In this paper we
evaluate these methods on 23 data sets using both neural networks and decision
trees as our classification algorithm. Our results clearly indicate a number of
conclusions. First, while Bagging is almost always more accurate than a single
classifier, it is sometimes much less accurate than Boosting. On the other
hand, Boosting can create ensembles that are less accurate than a single
classifier -- especially when using neural networks. Analysis indicates that
the performance of the Boosting methods is dependent on the characteristics of
the data set being examined. In fact, further results show that Boosting
ensembles may overfit noisy data sets, thus decreasing its performance.
Finally, consistent with previous studies, our work suggests that most of the
gain in an ensemble's performance comes in the first few classifiers combined;
however, relatively large gains can be seen up to 25 classifiers when Boosting
decision trees
Nucleon structure from 2+1-flavor dynamical DWF lattice QCD at nearly physical pion mass
Domain-wall fermions (DWF) is a lattice discretization for Dirac fields that
preserves continuum-like chiral and flavor symmetries that are essential in
hadron physics. RIKEN-BNL-Columbia (RBC) and UKQCD Collaborations have been
generating sets of realistic 2+1-flavor dynamical lattice quantum
chromodynamics (QCD) numerical ensembles with DWF quarks with strange mass set
almost exactly at its physical value via reweighing and degenerate up and down
mass set as light as practical. In this report the current status of the
nucleon-structure calculations using these ensembles are summarized.Comment: 7 pages, 5 figures, talk presented at Erice School "From Quarks and
Gluons to Hadrons and Nuclei,'' September 16-24, 2011, Erice, Sicil
Random Prism: An Alternative to Random Forests.
Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach. However, an alternative approach for the induction of rule based classifiers is the Prism family of algorithms. Prism algorithms produce modular classification rules that do not necessarily fit into a decision tree structure. Prism classification rulesets achieve a comparable and sometimes higher classification accuracy compared with decision tree classifiers, if the data is noisy and large. Yet Prism still suffers from overfitting on noisy and large datasets. In practice ensemble techniques tend to reduce the overfitting, however there exists no ensemble learner for modular classification rule inducers such as the Prism family of algorithms. This article describes the first development of an ensemble learner based on the Prism family of algorithms in order to enhance Prismās classification accuracy by reducing overfitting
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