Affiliations: [a] Department of Information Technology, Kongu Engineering College, Tamil Nadu, India | [b] Department of Computer Science and Engineering, Kongu Engineering College, Tamil Nadu, India | [c] Department of Computer Science and Engineering, Velalar College of Engineering and Technology, Tamil Nadu, India
Correspondence:
[*]
Corresponding author: R. Devi Priya, Department of Information Technology, Kongu Engineering College, Tamil Nadu, Pin-638 052, India. E-mail:[email protected]
Abstract: Time series datasets often suffer from the problem of non-ignorable
incompleteness which needs careful attention. Even though researchers
introduced many methods to handle the missing values, extra effort is needed
in searching suitable method for discrete and continuous attributes. This
paper proposes a new mechanism called as Bayesian Genetic Algorithm (BAGEL)
which is capable of handling missing values in both continuous and discrete
attributes in time series datasets using Bayesian analysis and Genetic
Algorithms. In BAGEL, Bayesian principles are used to model the data and
Genetic Algorithm is used to search the most accurate value that can be
estimated from the available data. The method is applied to impute the
missing values at different missing rates and the results produced are found
to be more accurate when compared with existing methods.
Keywords: Time series, Bayesian analysis, Genetic Algorithms, discrete, continuous attributes
