The effect of missing data on classification quality

Michael Feldman, Adir Even, Yisrael Parmet

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

Abstract

The field of data quality management has long recognized the negative impact of data quality defects on decision quality. In many decision scenarios, this negative impact can be largely attributed to the mediating role played by decision-support models - with defected data, the estimation of such a model becomes less reliable and, as a result, the likelihood of flawed decisions increases. Drawing on that argument, this study presents a methodology for assessing the impact of quality defects on the likelihood of flawed decisions. The methodology is first presented at a high level, and then extended for analyzing the impact of missing values on binary Linear Discriminant Analysis (LDA) classifiers. To conclude, we discuss possible directions for extensions and future directions.

Original languageEnglish
Title of host publicationProceedings of ICIQ 2012
Subtitle of host publication17th International Conference on Information Quality
EditorsLaure Berti-Equille, Isabelle Comyn-Wattiau, Monica Scannapieco
PublisherMIT
Pages229-242
Number of pages14
ISBN (Electronic)9781627483964
StatePublished - 1 Jan 2012
Event17th International Conference on Information Quality, ICIQ 2012 - Paris, France
Duration: 16 Nov 201217 Nov 2012

Conference

Conference17th International Conference on Information Quality, ICIQ 2012
Country/TerritoryFrance
CityParis
Period16/11/1217/11/12

Keywords

  • Classification
  • Data Quality
  • Decision Making
  • Linear Discriminant Analysis
  • Missing Values

ASJC Scopus subject areas

  • Information Systems
  • Safety, Risk, Reliability and Quality

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