Towards Rough Set Theory for Outliers Detection in Questionnaire Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F23%3A10253449" target="_blank" >RIV/61989100:27240/23:10253449 - isvavai.cz</a>

Výsledek na webu

<a href="https://link.springer.com/chapter/10.1007/978-3-031-42823-4_23" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-42823-4_23</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-42823-4_23" target="_blank" >10.1007/978-3-031-42823-4_23</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Towards Rough Set Theory for Outliers Detection in Questionnaire Data

Popis výsledku v původním jazyce

Manual processing of questionnaire surveys takes a lot of time and effort. This article aims at the automatic detection of corrupted or inappropriate responses in questionnaire data using unsupervised outliers detection methods. Unlike numerical data, which are usually assessed by distance-based methods, the entries in questionnaires need to be assessed from multiple perspectives. This paper proposes a novel algorithm utilizing the rough sets that capture relations among attributes/questions. The rough set theory is based on the granularity of data and is used to find combinations of attributes identifying the discernible questionnaires. The method is compared with standard and recent outlier detection algorithms that are based on distance, entropy, correlation, and probability. The tests are computed on the real-world HBSC dataset using several experiments. The rough set score computed on combinations of three attributes is preferred as it returns significant outliers that even reflect multiple perspectives investigated by other types of methods.

Název v anglickém jazyce

Towards Rough Set Theory for Outliers Detection in Questionnaire Data

Popis výsledku anglicky

Manual processing of questionnaire surveys takes a lot of time and effort. This article aims at the automatic detection of corrupted or inappropriate responses in questionnaire data using unsupervised outliers detection methods. Unlike numerical data, which are usually assessed by distance-based methods, the entries in questionnaires need to be assessed from multiple perspectives. This paper proposes a novel algorithm utilizing the rough sets that capture relations among attributes/questions. The rough set theory is based on the granularity of data and is used to find combinations of attributes identifying the discernible questionnaires. The method is compared with standard and recent outlier detection algorithms that are based on distance, entropy, correlation, and probability. The tests are computed on the real-world HBSC dataset using several experiments. The rough set score computed on combinations of three attributes is preferred as it returns significant outliers that even reflect multiple perspectives investigated by other types of methods.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Volume 14164

ISBN

978-3-031-42822-7

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

15

Strana od-do

310-324

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Tokio

Datum konání akce

22. 9. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—