Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12837
Title: Improving classifier performance based on AUC
Authors: Wong, AKS
Lee, JWT
Yeung, DS
Keywords: Graph theory
Learning (artificial intelligence)
Optimisation
Pattern classification
Performance evaluation
Text analysis
Issue Date: 2006
Publisher: IEEE
Source: 18th International Conference on Pattern Recognition, 2006 : ICPR 2006, 20-24 August 2006, Hong Kong, p. 268-271 How to cite?
Abstract: To evaluate the performance of text classifiers, we usually look at measures related to precision and recall, and most machine learning methods are optimized for these measures. In recent year, the use of receiver operating characteristics (ROC) graph and its extension area under the ROC curve (AUC) in gauging classifier performance has attracted much attention from the machine learning community. This measure is especially useful when a data set is imbalanced or when operating characteristics are unknown. Some researchers have started investigating the optimization of existing learning model for this new performance criterion. In this paper, we proposed modifications to the well-known weight updating text classifier sleeping-experts (SE) for AUC optimization. Our experiments show that through our new sampling and updating strategy we can improve the classifier both in terms of AUC and the traditional performance measures
URI: http://hdl.handle.net/10397/12837
ISBN: 0-7695-2521-0
ISSN: 1051-4651
DOI: 10.1109/ICPR.2006.705
Appears in Collections:Conference Paper

SFX Query Show full item record

SCOPUSTM   
Citations

2
Citations as of Dec 6, 2017

Page view(s)

40
Last Week
0
Last month
Citations as of Dec 18, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.