Míry kvality souborů pravidel získávaných z dat

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F08%3A00315192" target="_blank" >RIV/67985807:_____/08:00315192 - 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

Measures of Quality of Rulesets Extracted from Data

Popis výsledku v původním jazyce

The paper deals with quality measures of whole sets of rules extracted from data, as a counterpart to more commonly used measures of individual rules. This research has been motivated by increasingly frequent extraction of non-classification rules, suchas association rules and rules of observational logic, in real-world data mining tasks. The paper sketches the typology of rules extraction methods and of their rulesets, and recalls that quality measures for whole sets of rules have been so far used only in the case of classification rulesets. It then proposes three possible ways how such measures can be extended to general rulesets. The paper also recalls the possibility to measure the dependence of classification ruleset on parameters of the classification method by means of ROC curves, and proposes a generalization of ROC curves to general rulesets. Finally, a brief illustration on rulesets extracted by means of the method GUHA is given.

Název v anglickém jazyce

Measures of Quality of Rulesets Extracted from Data

Popis výsledku anglicky

The paper deals with quality measures of whole sets of rules extracted from data, as a counterpart to more commonly used measures of individual rules. This research has been motivated by increasingly frequent extraction of non-classification rules, suchas association rules and rules of observational logic, in real-world data mining tasks. The paper sketches the typology of rules extraction methods and of their rulesets, and recalls that quality measures for whole sets of rules have been so far used only in the case of classification rulesets. It then proposes three possible ways how such measures can be extended to general rulesets. The paper also recalls the possibility to measure the dependence of classification ruleset on parameters of the classification method by means of ROC curves, and proposes a generalization of ROC curves to general rulesets. Finally, a brief illustration on rulesets extracted by means of the method GUHA is given.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GA201%2F08%2F1744" target="_blank" >GA201/08/1744: Složitost perceptronových a jádrových sítí</a><br>

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2008

Kód důvěrnosti údajů

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

Název statě ve sborníku

Information Technologies - Applications and Theory

ISBN

978-80-969184-9-2

ISSN

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e-ISSN

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Počet stran výsledku

7

Strana od-do

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Název nakladatele

Prírodovedecká fakulta, Univerzita P.J. Šafárika

Místo vydání

Košice

Místo konání akce

Hrebienok

Datum konání akce

22. 9. 2008

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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