CLEMI-imputation evaluation

Anthony Chapman, Wei Pang, George Coghill

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Missing data is challenging enough without the added complexities posed by a lack of research in evaluating imputation. Not only could we potentially increase the impact and validity of studies from many different sectors (research, public and private), we also believe that by creating evaluation software, more researchers may be willing to use and justify using imputation methods. This paper aims to encourage further research for efficient imputation evaluation by defining a framework which could be used to optimise the way we impute datasets prior to data analysis. We propose a framework which uses a prototypical approach to create testing data and machine learning methods to create a new metric for evaluation. Preliminary results are presented which show how, for our dataset, records with less than 40% missingness could be used for analysis, increasing the amount of available data.

Original languageEnglish
Title of host publicationSACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages373-378
Number of pages6
ISBN (Print)9781538646403
DOIs
Publication statusPublished - 20 Aug 2018
Event12th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2018 - Timisoara, Romania
Duration: 17 May 201819 May 2018

Conference

Conference12th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2018
CountryRomania
CityTimisoara
Period17/05/1819/05/18

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Keywords

  • Clustering
  • Evaluating Imputation
  • Imputation
  • Missing Data
  • Prototypical Testing

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Control and Optimization

Cite this

Chapman, A., Pang, W., & Coghill, G. (2018). CLEMI-imputation evaluation. In SACI 2018 - IEEE 12th International Symposium on Applied Computational Intelligence and Informatics, Proceedings (pp. 373-378). [8440981] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SACI.2018.8440981