Towards Rough Set Theory for Outliers Detection in Questionnaire Data

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Towards Rough Set Theory for Outliers Detection in Questionnaire Data

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

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

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

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

Article name in the collection

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

Number of pages

15

Pages from-to

310-324

Publisher name

Springer

Place of publication

Cham

Event location

Tokio

Event date

Sep 22, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—