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
    Country/TerritoryViet Nam
    CityDanang
    Period13/12/1813/12/20

    Keywords

    • Bayes' Theorem
    • Item Guessing
    • Item Response Data

    ASJC Scopus subject areas

    • Industrial and Manufacturing Engineering

    Fingerprint

    Dive into the research topics of 'Inferring probability of guessing from item response data using bayes' theorem'. Together they form a unique fingerprint.

    Cite this