TY - GEN
T1 - Imputation of Missing Data Using PCA, Neuro-Fuzzy and Genetic Algorithms
AU - Hlalele, Nthabiseng
AU - Nelwamondo, Fulufhelo
AU - Marwala, Tshilidzi
PY - 2009
Y1 - 2009
N2 - This paper presents a method of imputing missing data that combines principal component analysis and neuro-fuzzy (PCA-NF) modeling in conjunction with genetic algorithms (GA). The ability of the model to impute missing data is tested using the South African HIV sero-prevalence dataset. The results indicate an average increase in accuracy from 60 % when using the neuro-fuzzy model independently to 99 % when the proposed model is used.
AB - This paper presents a method of imputing missing data that combines principal component analysis and neuro-fuzzy (PCA-NF) modeling in conjunction with genetic algorithms (GA). The ability of the model to impute missing data is tested using the South African HIV sero-prevalence dataset. The results indicate an average increase in accuracy from 60 % when using the neuro-fuzzy model independently to 99 % when the proposed model is used.
UR - http://www.scopus.com/inward/record.url?scp=70349113868&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-03040-6_59
DO - 10.1007/978-3-642-03040-6_59
M3 - Conference contribution
AN - SCOPUS:70349113868
SN - 3642030394
SN - 9783642030390
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 485
EP - 492
BT - Advances in Neuro-Information Processing - 15th International Conference, ICONIP 2008, Revised Selected Papers
T2 - 15th International Conference on Neuro-Information Processing, ICONIP 2008
Y2 - 25 November 2008 through 28 November 2008
ER -
