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 proceedingConference 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 languageEnglish
Title of host publicationLecture Notes in Electrical Engineering
PublisherSpringer Verlag
Pages448-456
Number of pages9
Volume280 LNEE
ISBN (Print)9783642416705
DOIs
Publication statusPublished - 2014
Event8th International Conference on Ubiquitous Information Technologies and Applications, CUTE 2013 - Danang, Viet Nam
Duration: 2013 Dec 182013 Dec 20

Publication series

NameLecture Notes in Electrical Engineering
Volume280 LNEE
ISSN (Print)18761100
ISSN (Electronic)18761119

Other

Other8th International Conference on Ubiquitous Information Technologies and Applications, CUTE 2013
CountryViet Nam
CityDanang
Period13/12/1813/12/20

Keywords

  • Bayes' Theorem
  • Item Guessing
  • Item Response Data

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

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  • Cite this

    Kim, B. W., Kim, J. M., & Lee, W. G. (2014). Inferring probability of guessing from item response data using bayes' theorem. In Lecture Notes in Electrical Engineering (Vol. 280 LNEE, pp. 448-456). (Lecture Notes in Electrical Engineering; Vol. 280 LNEE). Springer Verlag. https://doi.org/10.1007/978-3-642-41671-2_57