BOOTSTRAP AGGREGATING (BAGGING) REGRESI LOGISTIK ORDINAL UNTUK MENGKLASIFIKASIKAN STATUS GIZI BALITA DI KABUPATEN KLUNGKUNG

Entropy Ensemble Filter: A Modified Bootstrap Aggregating (Bagging) Procedure to Improve Efficiency in Ensemble Model Simulation

Over the past two decades, the Bootstrap AGGregatING (bagging) method has been widely used for improving simulation. The computational cost of this method scales with the size of the ensemble, but excessively reducing the ensemble size comes at the cost of reduced predictive performance. The novel procedure proposed in this study is the Entropy Ensemble Filter (EEF), which uses the most informa...

Bagging and the Bayesian Bootstrap

Bagging is a method of obtaining more robust predictions when the model class under consideration is unstable with respect to the data, i.e., small changes in the data can cause the predicted values to change significantly. In this paper, we introduce a Bayesian version of bagging based on the Bayesian bootstrap. The Bayesian bootstrap resolves a theoretical problem with ordinary bagging and of...

Double-bagging: combining classifiers by bootstrap aggregation

The combination of classi"ers leads to substantial reduction of misclassi"cation error in a wide range of applications and benchmark problems. We suggest using an out-of-bag sample for combining di0erent classi"ers. In our setup, a linear discriminant analysis is performed using the observations in the out-of-bag sample, and the corresponding discriminant variables computed for the observations...

Aggregating classifiers with ordinal response structure

HIT Dependency Parsing : Bootstrap Aggregating Heterogeneous Parsers

The paper describes our system of Shared Task on Parsing the Web. We only participate in dependency parsing task. A number of methods have been developed for dependency parsing. Each of the methods adopts very different view of dependency parsing, and each view can have its strengths and limitations. Thus system combination can have great potential to further improve the performance of dependen...