Rough Classifiers Sensitive to Costs Varying from Object to Object

Lenarcik, Andrzej; Piasta, Zdzisław

doi:10.1007/3-540-69115-4_31

Cited by 13 publications

(6 citation statements)

References 2 publications

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“…The idea we discuss in this paper is to let the cost depend on the single example and not only on the class of the example. This leads to the notion of example dependent costs, which was to our knowledge first formulated in [6]. Besides costs for misclassification, we consider costs for correct classification (gains are expressed as negative costs).…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machines with Example Dependent Costs

Brefeld

Geibel

Wysotzki

2003

Machine Learning: ECML 2003

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Classical learning algorithms from the fields of artificial neural networks and machine learning, typically, do not take any costs into account or allow only costs depending on the classes of the examples that are used for learning. As an extension of class dependent costs, we consider costs that are example, i.e. feature and class dependent. We present a natural cost-sensitive extension of the support vector machine (SVM) and discuss its relation to the Bayes rule. We also derive an approach for including example dependent costs into an arbitrary cost-insensitive learning algorithm by sampling according to modified probability distributions.

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Section: Introductionmentioning

confidence: 99%

Support Vector Machines with Example Dependent Costs

Brefeld

Geibel

Wysotzki

2003

Machine Learning: ECML 2003

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“…In practice, keeping a statistical perspective is necessary. The initial research followed this line, such as in [18], which is one of the pioneer works on the subject. Some years later, machine learning approaches were considered.…”

Section: Introductionmentioning

confidence: 99%

A Bayes Risk Minimization Machine for Example-Dependent Cost Classification

Lázaro

Figueiras-Vidal

2021

IEEE Trans. Cybern.

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“…This paper focuses in the most important one: the cost of classification errors. From that point of view, two types of CS problems can be distinguished: those that have class-dependent costs [3] (for instance, medical diagnosis problems) and others that have example-dependent costs [4] (e.g. loan application approval).…”

Section: Introductionmentioning

confidence: 99%

Multiclass Support Vector Machines With Example-Dependent Costs Applied to Plankton Biomass Estimation

González

Alvarez

Barranquero

et al. 2013

IEEE Trans. Neural Netw. Learning Syst.

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Abstract-In many applications, the mistakes made by an automatic classifier are not equal, they have different costs. These problems may be solved using a cost-sensitive learning approach. The main idea is not to minimize the number of errors, but the total cost produced by such mistakes. This paper presents a new multiclass costsensitive algorithm, in which each example has attached its corresponding misclassification cost. Our proposal is theoretically well-founded and is designed to optimize costsensitive loss functions. This research was motivated by a real-world problem, the biomass estimation of several plankton taxonomic groups. In this particular application, our method improves the performance of traditional multiclass classification approaches that optimize the accuracy.

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Rough Classifiers Sensitive to Costs Varying from Object to Object

Cited by 13 publications

References 2 publications

Support Vector Machines with Example Dependent Costs

Support Vector Machines with Example Dependent Costs

A Bayes Risk Minimization Machine for Example-Dependent Cost Classification

Multiclass Support Vector Machines With Example-Dependent Costs Applied to Plankton Biomass Estimation

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