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Cost-sensitive Learning (cost-sensitive + learning)

Distribution by Scientific Domains
Information Science and Computing100%

Selected Abstracts

ON MULTI-CLASS COST-SENSITIVE LEARNING

COMPUTATIONAL INTELLIGENCE, Issue 3 2010
Zhi-Hua Zhou
Rescaling,is possibly the most popular approach to cost-sensitive learning. This approach works by rebalancing the classes according to their costs, and it can be realized in different ways, for example, re-weighting or resampling the training examples in proportion to their costs, moving the decision boundaries of classifiers faraway from high-cost classes in proportion to costs, etc. This approach is very effective in dealing with two-class problems, yet some studies showed that it is often not so helpful on multi-class problems. In this article, we try to explore why the rescaling approach is often helpless on multi-class problems. Our analysis discloses that the rescaling approach works well when the costs are,consistent, while directly applying it to multi-class problems with,inconsistent,costs may not be a good choice. Based on this recognition, we advocate that before applying the rescaling approach, the,consistency,of the costs must be examined at first. If the costs are consistent, the rescaling approach can be conducted directly; otherwise it is better to apply rescaling after decomposing the multi-class problem into a series of two-class problems. An empirical study involving 20 multi-class data sets and seven types of cost-sensitive learners validates our proposal. Moreover, we show that the proposal is also helpful for class-imbalance learning. [source]

Cost-sensitive learning and decision making for massachusetts pip claim fraud data

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS, Issue 12 2004
Stijn Viaene
In this article, we investigate the issue of cost-sensitive classification for a data set of Massachusetts closed personal injury protection (PIP) automobile insurance claims that were previously investigated for suspicion of fraud by domain experts and for which we obtained cost information. After a theoretical exposition on cost-sensitive learning and decision-making methods, we then apply these methods to the claims data at hand to contrast the predictive performance of the documented methods for a selection of decision tree and rule learners. We use standard logistic regression and (smoothed) naive Bayes as benchmarks. © 2004 Wiley Periodicals, Inc. Int J Int Syst 19: 1197,1215, 2004. [source]

ON MULTI-CLASS COST-SENSITIVE LEARNING

COMPUTATIONAL INTELLIGENCE, Issue 3 2010
Zhi-Hua Zhou
Rescaling,is possibly the most popular approach to cost-sensitive learning. This approach works by rebalancing the classes according to their costs, and it can be realized in different ways, for example, re-weighting or resampling the training examples in proportion to their costs, moving the decision boundaries of classifiers faraway from high-cost classes in proportion to costs, etc. This approach is very effective in dealing with two-class problems, yet some studies showed that it is often not so helpful on multi-class problems. In this article, we try to explore why the rescaling approach is often helpless on multi-class problems. Our analysis discloses that the rescaling approach works well when the costs are,consistent, while directly applying it to multi-class problems with,inconsistent,costs may not be a good choice. Based on this recognition, we advocate that before applying the rescaling approach, the,consistency,of the costs must be examined at first. If the costs are consistent, the rescaling approach can be conducted directly; otherwise it is better to apply rescaling after decomposing the multi-class problem into a series of two-class problems. An empirical study involving 20 multi-class data sets and seven types of cost-sensitive learners validates our proposal. Moreover, we show that the proposal is also helpful for class-imbalance learning. [source]