Comparing rule mining approaches for classification with reasoning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F18%3A00324715" target="_blank" >RIV/68407700:21240/18:00324715 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Comparing rule mining approaches for classification with reasoning

Original language description

Classification serves an important role in domains such as network security or health care. Although these domains require understanding of the classifier’s decision, there are only a few classification methods trying to justify or explain their results. Classification rules and decision trees are generally considered comprehensible. Therefore, this study compares the classification performance and comprehensibility of a random forest classifier with classification rules extracted by Frequent Item Set Mining, Logical Item Set Mining and by the Explainer algorithm, which was previously proposed by the authors.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2018

Confidentiality

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

Article name in the collection

Proceedings of the 18th Conference Information Technologies - Applications and Theory (ITAT 2018)

ISBN

9781727267198

ISSN

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e-ISSN

1613-0073

Number of pages

7

Pages from-to

52-58

Publisher name

CEUR Workshop Proceedings

Place of publication

Aachen

Event location

Krompachy

Event date

Sep 21, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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