The evaluation of binary classification tasks in economical prediction

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F10%3A00168674" target="_blank" >RIV/62156489:43110/10:00168674 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

The evaluation of binary classification tasks in economical prediction

Popis výsledku v původním jazyce

In the area of economical classification tasks, the accuracy maximization is often used to evaluate classifier performance. Accuracy maximization (or error rate minimization) suffers from the assumption of equal false positive and false negative error costs. Furthermore, accuracy is not able to express true classifier performance under skewed class distribution. Due to these limitations, the use of accuracy on real tasks is questionable. In a real binary classification task, the difference between the costs of false positive and false negative error is usually critical. To overcome this issue, the Receiver Operating Characteristic (ROC) method in relation to decision-analytic principles can be used. One essential advantage of this method is the possibility of classifier performance visualization by means of a ROC graph. This paper presents concrete examples of binary classification, where the inadequacy of accuracy as the evaluation metric is shown, and on the same examples the ROC met

Název v anglickém jazyce

The evaluation of binary classification tasks in economical prediction

Popis výsledku anglicky

In the area of economical classification tasks, the accuracy maximization is often used to evaluate classifier performance. Accuracy maximization (or error rate minimization) suffers from the assumption of equal false positive and false negative error costs. Furthermore, accuracy is not able to express true classifier performance under skewed class distribution. Due to these limitations, the use of accuracy on real tasks is questionable. In a real binary classification task, the difference between the costs of false positive and false negative error is usually critical. To overcome this issue, the Receiver Operating Characteristic (ROC) method in relation to decision-analytic principles can be used. One essential advantage of this method is the possibility of classifier performance visualization by means of a ROC graph. This paper presents concrete examples of binary classification, where the inadequacy of accuracy as the evaluation metric is shown, and on the same examples the ROC met

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2010

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis

ISSN

1211-8516

e-ISSN

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Svazek periodika

LVIII

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

10

Strana od-do

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Kód UT WoS článku

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EID výsledku v databázi Scopus

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