@inproceedings{fddebd49416f4b20963d031fb4d24a28,
title = "Variance optimized bagging",
abstract = "We propose and study a newt echnique for aggregating an ensemble of bootstrapped classifiers. In this method we seek a linear combination of the base-classifiers such that the weights are optimized to reduce variance. Minimum variance combinations are computed using quadratic programming. This optimization technique is borrowed from Mathematical Finance where it is called Markowitz Mean-Variance Portfolio Optimization. We test the newmetho d on a number of binary classification problems from the UCI repository using a Support Vector Machine (SVM) as the base-classifier learning algorithm. Our results indicate that the proposed technique can consistently outperform Bagging and can dramatically improve the SVM performance even in cases where the Bagging fails to improve the base-classifier.",
author = "Philip Derbeko and Ran El-Yaniv and Ron Meir",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2002.; 13th European Conference on Machine Learning, ECML 2002 ; Conference date: 19-08-2002 Through 23-08-2002",
year = "2002",
month = jan,
day = "1",
doi = "10.1007/3-540-36755-1_6",
language = "English",
isbn = "9783540440369",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "60--72",
editor = "Tapio Elomaa and Heikki Mannila and Hannu Toivonen",
booktitle = "Machine Learning",
address = "Germany",
}