Dynamic classifier ensemble using classification confidence

How to combine the outputs from base classifiers is a key issue in ensemble learning. This paper presents a dynamic classifier ensemble method termed as DCE-CC. It dynamically selects a subset of classifiers for test samples according to classification confidence. The weights of base classifiers are...

Full description

Saved in:
Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2013-01, Vol.99, p.581-591
Main Authors: Li, Leijun, Zou, Bo, Hu, Qinghua, Wu, Xiangqian, Yu, Daren
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Classification confidence
Computer science
control theory
systems
Data processing. List processing. Character string processing
Dynamic classifier ensemble
Exact sciences and technology
Margin distribution
Memory organisation. Data processing
Software
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:How to combine the outputs from base classifiers is a key issue in ensemble learning. This paper presents a dynamic classifier ensemble method termed as DCE-CC. It dynamically selects a subset of classifiers for test samples according to classification confidence. The weights of base classifiers are learned by optimization of margin distribution on the training set, and the ordered aggregation technique is exploited to estimate the size of an appropriate subset. We examine the proposed fusion method on some benchmark classification tasks, where the stable nearest-neighbor rule and the unstable C4.5 decision tree algorithm are used for generating base classifiers, respectively. Compared with some other multiple classifier fusion algorithms, the experimental results show the effectiveness of our approach. Then we explain the experimental results from the view point of margin distribution.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2012.07.026