Int. J. Data Mining and Bioinformatics, Vol. 16, No. 3, 2016 183 Copyright © 2016 Inderscience Enterprises Ltd. Pre-processing of microarray gene expression data for classification using adaptive feature selection and imputation of non-ignorable missing values R. Devi Priya* Department of Information Technology, Kongu Engineering College, Erode, Tamil Nadu, India Email: scrpriya@gmail.com *Corresponding author R. Sivaraj Department of Computer Science and Engineering, Velalar College of Engineering and Technology, Erode, Tamil Nadu, India Email: rsivarajcse@gmail.com Abstract: Microarray datasets often contain many features and incomplete values. To address these issues, this paper introduces a method called Genetic Algorithm-Based Adaptive Feature Selection with Missing value Imputation (GAFSMI) with two contributions. First, for identifying the noteworthy features, Genetic Algorithm-Based Adaptive Feature Selection (GAFS) is proposed. Then for imputing the non-ignorable missing values, Bayesian Genetic Algorithm (BAGEL) integrating genetic algorithm with Bayesian principles is introduced. These two pre-processing steps generate the complete dataset with optimal feature subset to perform classification with better accuracy. The proposed algorithm is implemented on eight microarray datasets and it is observed that GAFS selects optimal feature subset with appreciable classification accuracy than other feature selection techniques. The imputation accuracy of BAGEL measured is found to be better than other standard imputation techniques at different missing rates (5% to 40%). Classification accuracy is improved in all the datasets processed with GAFS and BAGEL. Keywords: microarray data set; feature selection; missing values; genetic algorithm; classification. Reference to this paper should be made as follows: Devi Priya, R. and Sivaraj, R. (2016) ‘Pre-processing of microarray gene expression data for classification using adaptive feature selection and imputation of non-ignorable missing values’, Int. J. Data Mining and Bioinformatics, Vol. 16, No. 3, pp.183–204. Biographical notes: R. Devi Priya is an Assistant Professor in the Department of Information Technology, Kongu Engineering College. She has received her PhD from Anna University, Chennai, in 2013. She has published about 50 papers in national and international conferences and journals. Her research interests are data warehousing and mining and nature inspired algorithms.