Cost-Sensitive Online Classification

Both cost-sensitive classification and online learning have been extensively studied in data mining and machine learning communities, respectively. However, very limited study addresses an important intersecting problem, that is, "Cost-Sensitive Online Classification". In this paper, we fo...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 26; no. 10; pp. 2425 - 2438
Main Authors: Jialei Wang, Peilin Zhao, Hoi, Steven C. H.
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Algorithms
Classification
Data mining
Fasteners
Heuristic
Indexes
Machine learning
Mining methods and algorithms
Prediction algorithms
Sensitivity
Weight measurement
online gradient descent
Cost-sensitive classification
online anomaly detection
online learning
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary:Both cost-sensitive classification and online learning have been extensively studied in data mining and machine learning communities, respectively. However, very limited study addresses 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 directly optimizing cost-sensitive measures using online gradient descent techniques. Specifically, we propose two novel 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. Finally, we demonstrate the application of the proposed technique for solving several online anomaly detection tasks, showing that the proposed technique could be a highly efficient and effective tool to tackle cost-sensitive online classification tasks in various application domains.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2013.157