A generic neural network approach for filling missing data in data mining

Wei Wei, Ying Tang

Research output: Contribution to journalConference articlepeer-review

18 Scopus citations


The recent advances in data mining have produced algorithms for extracting hidden and potentially useful knowledge in large data sets, which are assumed to be complete and reliable. However, data suitable for mining comes from various sources, has different formats, and can have missing or incorrect values [2]. Incomplete data sets significantly distort mining results. Therefore, data preparation to taking care of missing or out-of-range values is very critical to knowledge discovery [8]. This paper proposes a generic framework for missing data imputation using neural networks, where two-stage filling algorithms are implemented. An empirical evaluation of this method through a large credit card data set is performed.

Original languageEnglish (US)
Pages (from-to)862-867
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
StatePublished - 2003
Externally publishedYes
EventSystem Security and Assurance - Washington, DC, United States
Duration: Oct 5 2003Oct 8 2003

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Hardware and Architecture


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