Decision making using incomplete data

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

Decision making often relies on relevant information extracted from data. To obtain such information, many data analysis techniques can be applied, including statistical analysis, clustering algorithms and modeling techniques using neural nets or machine learning. Unfortunately, in practice, missing data is common and most analysis techniques are not applicable to incomplete data. This paper investigates an approach to handling missing data, using heuristics, in a machine learning system, SORCER. We applied SORCER to decide if certain characteristics of COLIA1 gene mutations are or are not associated with fatal type of, OI (Osteogenesis imperfecta), a genetic disease. We compare the accuracies of SORCER's decisions with a high performing machine learning system, See5 with different percentages of missing data. The results show that average accuracies obtained from See5 tend to decline as the degree of incompleteness increases at a greater rate than those obtained from SORCER.

Original languageEnglish
Pages (from-to)182-187
Number of pages6
JournalConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume1
StatePublished - 2004
Event2004 IEEE International Conference on Systems, Man and Cybernetics, SMC 2004 - The Hague, Netherlands
Duration: Oct 10 2004Oct 13 2004

Keywords

  • Data Analysis
  • Machine learning
  • Missing data

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