A Survey of Selective Ensemble Learning Algorithms

In recent years,ensemble learning has received lots of attention in machine learning due to its potential to significantly improve the generalization capability of a learning system. With increasing number of ensemble members,however,the prediction speed of an ensemble machine decreases significantly and its storage need increases quickly.The aim of selective ensemble learning is to further improve the prediction accuracy of an ensemble machine,to enhance its prediction speed as well as to decrease its storage need.This paper presents a detailed review of the current selective ensemble learning algorithms and categorizes them into different classes according to their utilized selection strategy.Meanwhile,the main characteristics of each representative algorithm are studied.Finally,the future research directions of selective ensemble learning are discussed.