Current Science

R. Devi Priya ¹, R. Sivaraj ²

Affiliations
1 Department of Information Technology, Kongu Engineering College, Erode 638 052, IN
2 Department of Computer Science and Engineering, Velalar College of Engineering and Technology, Erode 638 002, IN

Abstract

Large microarray datasets usually contain many features with missing values. Inferences made from such incomplete datasets may be biased. To address this issue, we propose a novel preprocessing method called dynamic genetic algorithm-based feature selection with missing value imputation. The significant features are first identified using dynamic genetic algorithm-based feature selection and then the missing values are imputed using dynamic Bayesian genetic algorithm. The resulting complete microarray datasets with reduced features are used for classification, which results in better accuracy than the existing methods in eight microarray datasets.

Keywords

Microarray Dataset, Feature Selection, Missing Values, Genetic Algorithm.

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