@inproceedings{a1c0bcef991d43b2b2c9e950c59f8535,
title = "DIVACE: Diverse and accurate ensemble learning algorithm",
abstract = "In order for a neural network ensemble to generalise properly, two factors are considered vital. One is the diversity and the other is the accuracy of the networks that comprise the ensemble. There exists a tradeoff as to what should be the optimal measures of diversity and accuracy. The aim of this paper is to address this issue. We propose the DIVACE algorithm which tries to produce an ensemble as it searches for the optimum point on the diversity-accuracy curve. The DIVACE algorithm formulates the ensemble learning problem as a multi-objective problem explicitly. {\textcopyright} Springer-Verlag Berlin Heidelberg 2004.",
keywords = "Mutual Information, Pareto Front, Ensemble Member, Member Network, Neural Computation",
author = "Arjun CHANDRA and Xin YAO",
year = "2004",
doi = "10.1007/978-3-540-28651-6_91",
language = "English",
isbn = "9783540228813",
series = "Lecture Notes in Computer Science",
publisher = "Springer Berlin Heidelberg",
pages = "619--625",
editor = "YANG, {Zheng Rong} and Hujun YIN and EVERSON, {Richard M.}",
booktitle = "Intelligent Data Engineering and Automated Learning : IDEAL 2004 : 5th International Conference, Exeter, UK, August 25-27, 2004, Proceedings",
note = "5th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2004 ; Conference date: 25-08-2004 Through 27-08-2004",
}