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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Vydané v:IEEE transactions on knowledge and data engineering Ročník 26; číslo 10; s. 2425 - 2438
Hlavní autori: Jialei Wang, Peilin Zhao, Hoi, Steven C. H.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.10.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2013.157