文章基本信息

标题：On Adjustment Functions for Weight-Adjusted Voting-Based Ensembles of Classifiers
本地全文：下载
作者：Hsu, Kuo-Wei
期刊名称：Journal of Computers
印刷版ISSN：1796-203X
出版年度：2014
卷号：9
期号：7
页码：1547-1552
DOI：10.4304/jcp.9.7.1547-1552
语种：English
出版社：Academy Publisher
摘要：An ensemble of classifiers is a system consisting of multiple member classifiers which are trained individually and whose outcomes are aggregated into an overall outcome for a testing data instance. Voting is a common approach used to aggregate outcomes generated by member classifiers. Ensembles based on weighted voting have been studied for some time. However, the focus of most studies is more on weight assignment rather than on weight adjustment, whose basic idea is to increase the weights of votes from member classifiers performing better on data instances of higher difficulty. In this paper, we present our study on adjustment functions in each of which both the performance of a member classifier and the difficulty of a data set are determined nonlinearly. We report results from experiments conducted on several data sets, demonstrating the potential of the studied functions.
关键词：Classification;ensemble;voting