Test-Cost Sensitive Classification Based on Conditioned Loss Functions

Cebe, Mumin; Gündüz-Demir, Çiğdem

doi:10.1007/978-3-540-74958-5_52

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…(Turney 2000). There are many recent studies on the test cost (Chai et al 2004;Ling et al 2004;Cebe and Gunduz-Demir 2007), yet the most studied cost is the misclassification cost.…”

Section: Introductionmentioning

confidence: 99%

On Multi‐class Cost‐sensitive Learning

Zhou¹,

Liu²

2010

Computational Intelligence

269

144

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A popular approach to cost-sensitive learning is to rescale the classes according to their misclassification costs. Although this approach is effective in dealing with binary-class problems, recent studies show that it is often not so helpful when being applied to multiclass problems directly. This paper analyzes that why the traditional rescaling approach is often helpless on multi-class problems, which reveals that before applying rescaling, the consistency of the costs must be examined. Based on the analysis, a new approach is presented, which should be the choice if the user wants to use rescaling for multi-class cost-sensitive learning. Moreover, this paper shows that the proposed approach is helpful when unequal misclassification costs and class imbalance occur simultaneously, and can also be used to tackle pure class-imbalance learning. Thus, the proposed approach provides a unified framework for using rescaling to address multi-class cost-sensitive learning as well as multi-class class-imbalance learning.

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“…(Turney 2000). There are many recent studies on the test cost (Chai et al 2004;Ling et al 2004;Cebe and Gunduz-Demir 2007), yet the most studied cost is the misclassification cost.…”

Section: Introductionmentioning

confidence: 99%

On Multi‐class Cost‐sensitive Learning

Zhou¹,

Liu²

2010

Computational Intelligence

269

144

View full text Add to dashboard Cite

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“…(2)-(4). Our previous work defines the loss function and conditional risks similarly (Cebe and Gunduz-Demir, 2007). However, it requires using precise quantitative values of REWARD and PENALTY.…”

Section: Consistency-based Loss Functionsmentioning

confidence: 99%

“…This previous work uses an extension of a standard hidden Markov model (HMM) classifier where state transition probabilities are conditioned with feature extraction actions and values observed after feature extraction. This model can be used in two 4 Our previous work (Cebe and Gunduz-Demir, 2007) takes the cost of questionbased-features as zero (instead of a nominal cost of $1) and does not consider the common costs. Thus, its results for the Thyroid data set are slightly different than those in given Table 1.…”

Section: Comparisonsmentioning

confidence: 99%

“…In our approach, we use a Bayesian decision theoretical framework, in which (1) misclassification cost and cost of feature extraction are combined qualitatively and (2) the loss function is conditioned with the decisions taken using current and estimated information as well as their consistency. In our previous study, we also consider the consistency by conditioning our loss function with the consistency between current and estimated decisions (Cebe and Gunduz-Demir, 2007). However, this previous study combines misclassification cost and cost of feature extraction quantitatively, which requires the user to determine exact quantitative constants.…”

Section: Introductionmentioning

confidence: 99%

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Qualitative test-cost sensitive classification

Cebe

Gündüz-Demir

2010

Pattern Recognition Letters

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a b s t r a c tThis paper reports a new framework for test-cost sensitive classification. It introduces a new loss function definition, in which misclassification cost and cost of feature extraction are combined qualitatively and the loss is conditioned with current and estimated decisions as well as their consistency. This loss function definition is motivated with the following issues. First, for many applications, the relation between different types of costs can be expressed roughly and usually only in terms of ordinal relations, but not as a precise quantitative number. Second, the redundancy between features can be used to decrease the cost; it is possible not to consider a new feature if it is consistent with the existing ones. In this paper, we show the feasibility of the proposed framework for medical diagnosis problems. Our experiments demonstrate that this framework is efficient to significantly decrease feature extraction cost without decreasing accuracy.

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