Optimal artificial neural network architecture selection for bagging

Andersen, T.; Rimer, M.E.; Martinez, Tony

doi:10.1109/ijcnn.2001.939460

Cited by 1 publication

(1 citation statement)

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“…While bagging had been successfully applied to different classification and regression problems [2,6,7], random aggregating is almost ideal, because in most cases the true distribution P is unknown and often only small data sets are available.…”

Section: Subsampled Baggingmentioning

confidence: 99%

Random Aggregated and Bagged Ensembles of SVMs: An Empirical Bias–Variance Analysis

Valentini

2004

Multiple Classifier Systems

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Abstract. Bagging can be interpreted as an approximation of random aggregating, an ideal ensemble method by which base learners are trained using data sets randomly drawn according to an unknown probability distribution. An approximate realization of random aggregating can be obtained through subsampled bagging, when large training sets are available. In this paper we perform an experimental bias-variance analysis of bagged and random aggregated ensembles of Support Vector Machines, in order to quantitatively evaluate their theoretical variance reduction properties. Experimental results with small samples show that random aggregating, implemented through subsampled bagging, reduces the variance component of the error by about 90%, while bagging, as expected, achieves a lower reduction. Bias-variance analysis explains also why ensemble methods based on subsampling techniques can be successfully applied to large data mining problems.

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Section: Subsampled Baggingmentioning

confidence: 99%