34,296 research outputs found
Improving ECG Classification Accuracy Using an Ensemble of Neural Network Modules
This paper illustrates the use of a combined neural network model based on Stacked Generalization method for classification of electrocardiogram (ECG) beats. In conventional Stacked Generalization method, the combiner learns to map the base classifiers' outputs to the target data. We claim adding the input pattern to the base classifiers' outputs helps the combiner to obtain knowledge about the input space and as the result, performs better on the same task. Experimental results support our claim that the additional knowledge according to the input space, improves the performance of the proposed method which is called Modified Stacked Generalization. In particular, for classification of 14966 ECG beats that were not previously seen during training phase, the Modified Stacked Generalization method reduced the error rate for 12.41% in comparison with the best of ten popular classifier fusion methods including Max, Min, Average, Product, Majority Voting, Borda Count, Decision Templates, Weighted Averaging based on Particle Swarm Optimization and Stacked Generalization
GA-stacking: Evolutionary stacked generalization
Stacking is a widely used technique for combining classifiers and improving prediction accuracy. Early research in Stacking showed that selecting the right classifiers, their parameters and the meta-classifiers was a critical issue. Most of the research on this topic hand picks the right combination of classifiers and their parameters. Instead of starting from these initial strong assumptions, our approach uses genetic algorithms to search for good Stacking configurations. Since this can lead to overfitting, one of the goals of this paper is to empirically evaluate the overall efficiency of the approach. A second goal is to compare our approach with the current best Stacking building techniques. The results show that our approach finds Stacking configurations that, in the worst case, perform as well as the best techniques, with the advantage of not having to manually set up the structure of the Stacking system.This work has been partially supported by the Spanish MCyT under projects TRA2007-67374-C02-02
and TIN-2005-08818-C04. Also, it has been supported under MEC grant by TIN2005-08945-C06-05.
We thank anonymous reviewers for their helpful comments.Publicad
On r-stacked triangulated manifolds
The notion of r-stackedness for simplicial polytopes was introduced by
McMullen and Walkup in 1971 as a generalization of stacked polytopes. In this
paper, we define the r-stackedness for triangulated homology manifolds and
study their basic properties. In addition, we find a new necessary condition
for face vectors of triangulated manifolds when all the vertex links are
polytopal.Comment: 15 page
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting
Ensembling is among the most popular tools in machine learning (ML) due to
its effectiveness in minimizing variance and thus improving generalization.
Most ensembling methods for black-box base learners fall under the umbrella of
"stacked generalization," namely training an ML algorithm that takes the
inferences from the base learners as input. While stacking has been widely
applied in practice, its theoretical properties are poorly understood. In this
paper, we prove a novel result, showing that choosing the best stacked
generalization from a (finite or finite-dimensional) family of stacked
generalizations based on cross-validated performance does not perform "much
worse" than the oracle best. Our result strengthens and significantly extends
the results in Van der Laan et al. (2007). Inspired by the theoretical
analysis, we further propose a particular family of stacked generalizations in
the context of probabilistic forecasting, each one with a different sensitivity
for how much the ensemble weights are allowed to vary across items, timestamps
in the forecast horizon, and quantiles. Experimental results demonstrate the
performance gain of the proposed method.Comment: ICML 202
Stacking classifiers for anti-spam filtering of e-mail
We evaluate empirically a scheme for combining classifiers, known as stacked
generalization, in the context of anti-spam filtering, a novel cost-sensitive
application of text categorization. Unsolicited commercial e-mail, or "spam",
floods mailboxes, causing frustration, wasting bandwidth, and exposing minors
to unsuitable content. Using a public corpus, we show that stacking can improve
the efficiency of automatically induced anti-spam filters, and that such
filters can be used in real-life applications
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