Abstract
In this paper we present a novel technique called iDMI that imputes missing values of a data set by combining a decision tree algorithm (DT) and an expectation-maximization(EMI) algorithm. We first divide a data set into horizontal segmentsthrough applying a DT algorithm such as C4.5, and thenapply an EMI algorithm on each segment in order to impute themissing values belong to the segment. If all numerical attribute values of a record are missing then we impute them by the meanvalues of the attributes of the records belong to a segment wherethe record falls in, and thereby reduce the computational time complexity of iDMI compare to an existing technique calledDMI which calculate the mean value of an attribute by using all records of a data set. We evaluate the performance of iDMIover three high quality existing techniques on two real data sets in terms of four evaluation criteria. Our initial experimental results, including several statistical significance analysis, indicatethe superiority of iDMI over the existing techniques.
Original language | English |
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Title of host publication | Proceedings of the 16th International Conference on Computer and Information Technology |
Subtitle of host publication | ICCIT 2013 |
Editors | Rameswar Debnath |
Place of Publication | United States |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 496-501 |
Number of pages | 6 |
ISBN (Electronic) | 9781479934980 |
DOIs | |
Publication status | Published - 2014 |
Event | 16th International Conference on Computer and Information Technology: ICCIT 2013 - Khulna University, Khulna, Bangladesh Duration: 08 Mar 2014 → 10 Mar 2014 https://web.archive.org/web/20130409043745/http://www.iccit.org.bd/2013/ |
Conference
Conference | 16th International Conference on Computer and Information Technology |
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Country/Territory | Bangladesh |
City | Khulna |
Period | 08/03/14 → 10/03/14 |
Internet address |