Inferring probability of guessing from item response data using bayes' theorem

Byoung Wook Kim; Ja Mee Kim; Won Gyu Lee

doi:10.1007/978-3-642-41671-2_57

Inferring probability of guessing from item response data using bayes' theorem

Byoung Wook Kim, Ja Mee Kim, Won Gyu Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Outlier detection is a primary step in many data mining applications. Outlier means a marked response data correctly by guessing in item response data. Guessing an answer or a judgment about something without being sure of all the facts act as a noise in data mining. It is important to clean noise data for producing good results in data mining. In order to clean noise data, it is needed to detect correct answers marked by guessing among item response data. In this paper, we present a Bayesian approach to infer a probability of guessing for items.

Original language	English
Title of host publication	Lecture Notes in Electrical Engineering
Publisher	Springer Verlag
Pages	448-456
Number of pages	9
Volume	280 LNEE
ISBN (Print)	9783642416705
DOIs	https://doi.org/10.1007/978-3-642-41671-2_57
Publication status	Published - 2014
Event	8th International Conference on Ubiquitous Information Technologies and Applications, CUTE 2013 - Danang, Viet Nam Duration: 2013 Dec 18 → 2013 Dec 20

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	280 LNEE
ISSN (Print)	18761100
ISSN (Electronic)	18761119

Other

Other	8th International Conference on Ubiquitous Information Technologies and Applications, CUTE 2013
Country/Territory	Viet Nam
City	Danang
Period	13/12/18 → 13/12/20

Keywords

Bayes' Theorem
Item Guessing
Item Response Data

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

Access to Document

10.1007/978-3-642-41671-2_57

Cite this

Kim, BW, Kim, JM & Lee, WG 2014, Inferring probability of guessing from item response data using bayes' theorem. in Lecture Notes in Electrical Engineering. vol. 280 LNEE, Lecture Notes in Electrical Engineering, vol. 280 LNEE, Springer Verlag, pp. 448-456, 8th International Conference on Ubiquitous Information Technologies and Applications, CUTE 2013, Danang, Viet Nam, 13/12/18. https://doi.org/10.1007/978-3-642-41671-2_57

@inproceedings{e3ef827439c44e2e86c5c8c5ce071733,

title = "Inferring probability of guessing from item response data using bayes' theorem",

abstract = "Outlier detection is a primary step in many data mining applications. Outlier means a marked response data correctly by guessing in item response data. Guessing an answer or a judgment about something without being sure of all the facts act as a noise in data mining. It is important to clean noise data for producing good results in data mining. In order to clean noise data, it is needed to detect correct answers marked by guessing among item response data. In this paper, we present a Bayesian approach to infer a probability of guessing for items.",

keywords = "Bayes' Theorem, Item Guessing, Item Response Data",

author = "Kim, {Byoung Wook} and Kim, {Ja Mee} and Lee, {Won Gyu}",

year = "2014",

doi = "10.1007/978-3-642-41671-2_57",

language = "English",

isbn = "9783642416705",

volume = "280 LNEE",

series = "Lecture Notes in Electrical Engineering",

publisher = "Springer Verlag",

pages = "448--456",

booktitle = "Lecture Notes in Electrical Engineering",

note = "8th International Conference on Ubiquitous Information Technologies and Applications, CUTE 2013 ; Conference date: 18-12-2013 Through 20-12-2013",

}

TY - GEN

T1 - Inferring probability of guessing from item response data using bayes' theorem

AU - Kim, Byoung Wook

AU - Kim, Ja Mee

AU - Lee, Won Gyu

PY - 2014

Y1 - 2014

N2 - Outlier detection is a primary step in many data mining applications. Outlier means a marked response data correctly by guessing in item response data. Guessing an answer or a judgment about something without being sure of all the facts act as a noise in data mining. It is important to clean noise data for producing good results in data mining. In order to clean noise data, it is needed to detect correct answers marked by guessing among item response data. In this paper, we present a Bayesian approach to infer a probability of guessing for items.

AB - Outlier detection is a primary step in many data mining applications. Outlier means a marked response data correctly by guessing in item response data. Guessing an answer or a judgment about something without being sure of all the facts act as a noise in data mining. It is important to clean noise data for producing good results in data mining. In order to clean noise data, it is needed to detect correct answers marked by guessing among item response data. In this paper, we present a Bayesian approach to infer a probability of guessing for items.

KW - Bayes' Theorem

KW - Item Guessing

KW - Item Response Data

UR - http://www.scopus.com/inward/record.url?scp=84958543585&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84958543585&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-41671-2_57

DO - 10.1007/978-3-642-41671-2_57

M3 - Conference contribution

AN - SCOPUS:84958543585

SN - 9783642416705

VL - 280 LNEE

T3 - Lecture Notes in Electrical Engineering

SP - 448

EP - 456

BT - Lecture Notes in Electrical Engineering

PB - Springer Verlag

T2 - 8th International Conference on Ubiquitous Information Technologies and Applications, CUTE 2013

Y2 - 18 December 2013 through 20 December 2013

ER -

Inferring probability of guessing from item response data using bayes' theorem

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this