Analyzing bagging

Analyzing Bagging

Bagging is one of the most effective computationally intensive procedures to improve on unstable estimators or classifiers, useful especially for high dimensional data set problems. Here we formalize the notion of instability and derive theoretical results to analyze the variance reduction effect of bagging (or variants thereof) in mainly hard decision problems, which include estimation after t...

Bagging Random Tree for Analyzing Breast Cancer Survival

Building the survivability prediction models is a challenging task because they provide an important approach to assessing risk and prognosis. In this paper, we investigated the performance of combining of the Bagging with several weak learners to build 5-accurate breast cancer survivability prediction models from the Srinagarind hospital database in Thailand. These models could assist medical ...

Bagging Predictors Bagging Predictors

Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and ...

Bagging Regularizes

Intuitively, we expect that averaging — or bagging — different regressors with low correlation should smooth their behavior and be somewhat similar to regularization. In this note we make this intuition precise. Using an almost classical definition of stability, we prove that a certain form of averaging provides generalization bounds with a rate of convergence of the same order as Tikhonov regu...

Explaining Bagging