Machine Learning
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Randomizing Outputs to Increase Prediction Accuracy
Machine Learning
Database-friendly random projections
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Machine Learning
Knowledge Acquisition form Examples Vis Multiple Models
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Stacking Bagged and Dagged Models
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Inference for the Generalization Error
Machine Learning
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Improving multiclass pattern recognition with a co-evolutionary RBFNN
Pattern Recognition Letters
Ensembles of jittered association rule classifiers
Data Mining and Knowledge Discovery
A hybrid particle swarm optimization and its application in neural networks
Expert Systems with Applications: An International Journal
Ensemble approaches for regression: A survey
ACM Computing Surveys (CSUR)
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Bagging is an ensemble learning method that has proved to be a useful tool in the arsenal of machine learning practitioners. Commonly applied in conjunction with decision tree learners to build an ensemble of decision trees, it often leads to reduced errors in the predictions when compared to using a single tree. A single tree is built from a training set of size N. Bagging is based on the idea that, ideally, we would like to eliminate the variance due to a particular training set by combining trees built from all training sets of size N. However, in practice, only one training set is available, and bagging simulates this platonic method by sampling with replacement from the original training data to form new training sets. In this paper we pursue the idea of sampling from a kernel density estimator of the underlying distribution to form new training sets, in addition to sampling from the data itself. This can be viewed as “smearing out” the resampled training data to generate new datasets, and the amount of “smear” is controlled by a parameter. We show that the resulting method, called “input smearing”, can lead to improved results when compared to bagging. We present results for both classification and regression problems.