Please use this identifier to cite or link to this item:
Title: Cost-sensitive online classification
Authors: Hoi, Steven C. H.
Wang, Jialei.
Zhao, Peilin.
Keywords: DRNTU::Engineering::Computer science and engineering
Issue Date: 2012
Abstract: Both cost-sensitive classification and online learning have been studied extensively in data mining and machine learning communities, respectively. It is a bit surprising that there was very limited comprehensive study for addressing an important intersecting problem, that is, cost-sensitive online classification. In this paper, we formally study this problem, and propose a new framework for cost-sensitive online classification by exploiting the idea of online gradient descent techniques. Based on the framework, we propose a family of cost-sensitive online classification algorithms, which are designed to directly optimize two well-known cost-sensitive measures: (i) maximization of weighted sum of sensitivity and specificity, and (ii) minimization of weighted misclassification cost. We analyze the theoretical bounds of the cost-sensitive measures made by the proposed algorithms, and extensively examine their empirical performance on a variety of cost-sensitive online classification tasks.
DOI: 10.1109/ICDM.2012.116
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Conference Papers

Citations 10

Updated on Mar 7, 2021

Web of ScienceTM
Citations 10

Updated on Mar 3, 2021

Page view(s) 50

Updated on Sep 28, 2022

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.