New Bootstrap Applications in Supervised Learning
Getúlio José Amorim do Amaral, Marcelo R.P. Ferreira · Communications in Statistics - Simulation and Computation · 2008
Some bootstrap and boosting methods for problems related to classification are introduced in this article. The first method chooses better boosting weights by using a bootstrap search algorithm. The second method is a good way to define a classification frontier. A new formulation for boosting in linear discriminant analysis is given. Since in this new formulation the uncertainty is represented by the weighted covariance matrix, it is more appropriate from the conceptual point of view. Simulation results show that the proposed methods perform well in data analysis.