BAGEL – A novel hybrid technique for missing value estimation in mixed attribute datasets

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 tech...

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Bibliographic Details
Published inAi communications Vol. 29; no. 2; pp. 395 - 396
Main Author Devi Priya, R.
Format Journal Article
LanguageEnglish
Published Amsterdam IOS Press BV 02.03.2016
Subjects
Bayesian analysis
Datasets
Genetic algorithms
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Summary: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.
ISSN:0921-7126
1875-8452
DOI:10.3233/AIC-150660