Dynamic Genetic Algorithm-Based Feature Selection and Incomplete Value Imputation for Microarray Classification

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.