TY - GEN
T1 - An analysis of missing data treatment methods and their application to health care dataset
AU - Liu, Peng
AU - El-Darzi, Elia
AU - Lei, Lei
AU - Vasilakis, Christos
AU - Chountas, Panagiotis
AU - Huang, Wei
PY - 2005/12/1
Y1 - 2005/12/1
N2 - It is well accepted that many real-life datasets are full of missing data. In this paper we introduce, analyze and compare several well known treatment methods for missing data handling and propose new methods based on Naive Bayesian classifier to estimate and replace missing data. We conduct extensive experiments on datasets from UCI to compare these methods. Finally we apply these models to a geriatric hospital dataset in order to assess their effectiveness on a real-life dataset.
AB - It is well accepted that many real-life datasets are full of missing data. In this paper we introduce, analyze and compare several well known treatment methods for missing data handling and propose new methods based on Naive Bayesian classifier to estimate and replace missing data. We conduct extensive experiments on datasets from UCI to compare these methods. Finally we apply these models to a geriatric hospital dataset in order to assess their effectiveness on a real-life dataset.
UR - http://www.scopus.com/inward/record.url?scp=26944438304&partnerID=8YFLogxK
M3 - Chapter in a published conference proceeding
AN - SCOPUS:26944438304
SN - 354027894X
SN - 9783540278948
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 583
EP - 590
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
T2 - 1st International Conference on Advanced Data Mining and Applications, ADMA 2005
Y2 - 22 July 2005 through 24 July 2005
ER -