Predictive Ensemble Pruning by Expectation Propagation

@article{Chen2009PredictiveEP,
  title={Predictive Ensemble Pruning by Expectation Propagation},
  author={Huanhuan Chen and Peter Ti{\~n}o and Xin Yao},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2009},
  volume={21},
  pages={999-1013}
}
An ensemble is a group of learners that work together as a committee to solve a problem. The existing ensemble learning algorithms often generate unnecessarily large ensembles, which consume extra computational resource and may degrade the generalization performance. Ensemble pruning algorithms aim to find a good subset of ensemble members to constitute a small ensemble, which saves the computational resource and performs as well as, or better than, the unpruned ensemble. This paper introduces… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 39 CITATIONS

Ensemble Pruning via Margin Maximization

VIEW 4 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Joint sparse learning for classification ensemble

VIEW 4 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Evolving Diverse Ensembles Using Genetic Programming for Classification With Unbalanced Data

VIEW 4 EXCERPTS
CITES BACKGROUND & METHODS

Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles

VIEW 1 EXCERPT
CITES METHODS

A Divide-and-Conquer-Based Ensemble Classifier Learning by Means of Many-Objective Optimization

VIEW 1 EXCERPT
CITES BACKGROUND

Feature Extraction Based on Support Vector Data Description

The Optimized Selection of Base-Classifiers for Ensemble Classification using a Multi-Objective Genetic Algorithm

VIEW 2 EXCERPTS
CITES METHODS & BACKGROUND

References

Publications referenced by this paper.
SHOWING 1-10 OF 40 REFERENCES

Pruning Adaptive Boosting

VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

Bagging predictors

VIEW 12 EXCERPTS
HIGHLY INFLUENTIAL

UCI Machine Learning Repository

VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

A Probabilistic Ensemble Pruning Algorithm

VIEW 3 EXCERPTS

Ensembling neural networks: Many could be better than all

VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

Analysis of Sparse Bayesian Learning

VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

Expectation Propagation for approximate Bayesian inference

VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

A Brief Introduction to Boosting

VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Ensemble Pruning Via Semi-definite Programming

VIEW 1 EXCERPT