Databases often contain missing values caused in different scenarios. Even though many methods are available to treat such missing values, they are specific to only certain types of missingness and are not commonly applicable to all scenarios. To address this issue, this thesis proposes a novel technique called Bayesian Genetic Algorithm (BAGEL) which combines both Bayesian principles and Genetic Algorithm to impute values in different kinds of missing scenarios and different kinds of attributes in mixed attribute datasets.
A.Azadeh, S.Asadzadeh, R.Jafari-Marandi, S.Nazari-Shirkouhi, G.Khoshkhou, S.Talebid and A.Naghavi, Optimum estimation of missing values in randomized complete block design by genetic algorithm, Knowledge-Based Systems37(1) (2013), 37–47.
2.
R.Devi Priya, BAGEL – A novel hybrid technique for missing value estimation in mixed attribute datasets, PhD thesis, Anna University, 2013.
3.
R.Devi Priya and S.Kuppuswami, A genetic algorithm based approach for imputing missing discrete attribute values in databases, WSEAS Transactions on Information Science and Applications9(6) (2012), 169–178.
4.
R.Devi Priya and S.Kuppuswami, Drawing inferences from clinical studies with missing values using genetic algorithm, International Journal of Bioinformatics Research and Applications10(6) (2014), 613–627.
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R.DeviPriya and S.Kuppuswami, A novel approach for imputation of missing continuous attribute values in databases using genetic algorithm, International Journal of Information Technology and Management14(2-3) (2015), 185–200.
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R.Devi Priya, S.Kuppuswami and S.Makesh Kumar, A genetic algorithm approach for non-ignorable missing data, International Journal of Computer Applications20(4) (2011), 37–41.
7.
H.Xie, Analyzing longitudinal clinical trial data with nonignorable missingness and unknown missingness reasons, Computational Statistics and Data Analysis56(5) (2012), 1287–1300.
