Ensemble learning aims to improve generalization ability by using multiple base learners. It is well-known that to construct a good ensemble, the base learners should be accurate a...
— Ensembles are often capable of greater predictive accuracy than any of their individual members. One key attribute of ensembles’ success is the notion of diversity. However, ...
The nearest-neighbour (1NN) classifier has long been used in pattern recognition, exploratory data analysis, and data mining problems. A vital consideration in obtaining good res...
A novel method for creating diverse ensembles of image classifiers is proposed. The idea is that, for each base image classifier in the ensemble, a random image transformation is g...
In this paper, we present two ensemble learning algorithms which make use of boostrapping and out-of-bag estimation in an attempt to inherit the robustness of bagging to overfitti...