Cost-conscious classifier ensembles

Demir, Cigdem; Alpaydın, Ethem

doi:10.1016/j.patrec.2005.03.028

Cited by 21 publications

(23 citation statements)

References 9 publications

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“…Another possibility is to also include the other types of cost (e.g., delay cost (Sheng and Ling, 2006) and computational cost (Demir and Alpaydin, 2005)) into the problem formulation.…”

Section: Resultsmentioning

confidence: 99%

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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“…Another possibility is to also include the other types of cost (e.g., delay cost (Sheng and Ling, 2006) and computational cost (Demir and Alpaydin, 2005)) into the problem formulation.…”

Section: Resultsmentioning

confidence: 99%

Qualitative test-cost sensitive classification

Cebe

Gündüz-Demir

2010

Pattern Recognition Letters

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“…The random subspace method [16] trains different experts with different subsets of a given feature set. Different representations of the same input make different characteristics explicit and therefore accuracy may be improved by combination [1,11]. It has also been proposed to generate an ensemble of fuzzy decision trees and take their combination for better accuracy [45].…”

Section: Introductionmentioning

confidence: 99%

“…That is, we are interested in both pruning the inaccurate ones and also to keep a check on complexity, we want to prune the redundant. ''Diversity'' measures have been proposed [23,22] and one possibility is to have an incremental, forward search where we add a classifier to an ensemble if it is diverse or adds to accuracy [9,11,35,49,42], or another possibility is to have a decremental, backward search where a classifier is removed or pruned if it is not diverse enough or if its removal does not increase error [30,27]. In this work, we propose an alternative method which combines base classifiers using principal component analysis (PCA) to get uncorrelated eigenclassifiers.…”

Section: Introductionmentioning

confidence: 99%

Eigenclassifiers for combining correlated classifiers

Ulas

Yıldız

Alpaydın

2012

Information Sciences

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“…Subsequently, we train the estimators to learn these posteriors by using only the previously extracted features. Note that similar posterior probability estimations have been achieved by using linear perceptrons [4] and dynamic Bayesian networks [11].…”

Section: Methodsmentioning

confidence: 82%

“…Compared to the misclassification cost, the other types are much less studied. The cost of computation includes both static complexity, which arises from the size of a computer program [3], and dynamic complexity, which is incurred during training and testing a classifier [4]. The cost of feature extraction arises from the effort of acquiring a feature.…”

Section: Introductionmentioning

confidence: 99%

Test-Cost Sensitive Classification Based on Conditioned Loss Functions

Cebe

Gündüz-Demir

Machine Learning: ECML 2007

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Abstract. We report a novel approach for designing test-cost sensitive classifiers that consider the misclassification cost together with the cost of feature extraction utilizing the consistency behavior for the first time. In this approach, we propose to use a new Bayesian decision theoretical framework in which the loss is conditioned with the current decision and the expected decisions after additional features are extracted as well as the consistency among the current and expected decisions. This approach allows us to force the feature extraction for samples for which the current and expected decisions are inconsistent. On the other hand, it forces not to extract any features in the case of consistency, leading to less costly but equally accurate decisions. In this work, we apply this approach to a medical diagnosis problem and demonstrate that it reduces the overall feature extraction cost up to 47.61 percent without decreasing the accuracy.

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Cost-conscious classifier ensembles

Cited by 21 publications

References 9 publications

Qualitative test-cost sensitive classification

Qualitative test-cost sensitive classification

Eigenclassifiers for combining correlated classifiers

Test-Cost Sensitive Classification Based on Conditioned Loss Functions

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