Learning from multiple observers with unknown expertise

Han Xiao, Huang Xiao, Claudia Eckert

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Scopus citations

Abstract

Internet has emerged as a powerful technology for collecting labeled data from a large number of users around the world at very low cost. Consequently, each instance is often associated with a handful of labels, precluding any assessment of an individual user's quality. We present a probabilistic model for regression when there are multiple yet some unreliable observers providing continuous responses. Our approach simultaneously learns the regression function and the expertise of each observer that allow us to predict the ground truth and observers' responses on the new data. Experimental results on both synthetic and real-world data sets indicate that the proposed method has clear advantages over "taking the average" baseline and some state-of-art models.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings
Pages595-606
Number of pages12
EditionPART 1
DOIs
StatePublished - 2013
Event17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013 - Gold Coast, QLD, Australia
Duration: 14 Apr 201317 Apr 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7818 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013
Country/TerritoryAustralia
CityGold Coast, QLD
Period14/04/1317/04/13

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