Title
Cost-sensitive learning with conditional Markov networks
Abstract
There has been a recent, growing interest in classification and link prediction in structured domains. Methods such as conditional random fields and relational Markov networks support flexible mechanisms for modeling correlations due to the link structure. In addition, in many structured domains, there is an interesting structure in the risk or cost function associated with different misclassifications. There is a rich tradition of cost-sensitive learning applied to unstructured (IID) data. Here we propose a general framework which can capture correlations in the link structure and handle structured cost functions. We present two new cost-sensitive structured classifiers based on maximum entropy principles. The first determines the cost-sensitive classification by minimizing the expected cost of misclassification. The second directly determines the cost-sensitive classification without going through a probability estimation step. We contrast these approaches with an approach which employs a standard 0/1-loss structured classifier to estimate class conditional probabilities followed by minimization of the expected cost of misclassification and with a cost-sensitive IID classifier that does not utilize the correlations present in the link structure. We demonstrate the utility of our cost-sensitive structured classifiers with experiments on both synthetic and real-world data.
Year
DOI
Venue
2006
https://doi.org/10.1007/s10618-008-0090-5
Data Mining and Knowledge Discovery
Keywords
Field
DocType
Cost-sensitive learning,Machine learning,Markov networks,Structured output spaces
Structured support vector machine,Data mining,Maximum-entropy Markov model,Computer science,Structured prediction,Artificial intelligence,Classifier (linguistics),CRFS,Conditional random field,Pattern recognition,Markov chain,Principle of maximum entropy,Machine learning
Conference
Volume
Issue
ISSN
17
2
1384-5810
Citations 
PageRank 
References 
11
0.62
34
Authors
2
Name
Order
Citations
PageRank
Prithviraj Sen183738.24
Lise Getoor24365320.21