TY - JFULL AU - Younies Mahmoud and Mai Mabrouk and Elsayed Sallam PY - 2015/2/ TI - Imputation Technique for Feature Selection in Microarray Data Set T2 - International Journal of Bioengineering and Life Sciences SP - 305 EP - 310 VL - 9 SN - 1307-6892 UR - https://publications.waset.org/pdf/10000619 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 97, 2015 N2 - Analyzing DNA microarray data sets is a great challenge, which faces the bioinformaticians due to the complication of using statistical and machine learning techniques. The challenge will be doubled if the microarray data sets contain missing data, which happens regularly because these techniques cannot deal with missing data. One of the most important data analysis process on the microarray data set is feature selection. This process finds the most important genes that affect certain disease. In this paper, we introduce a technique for imputing the missing data in microarray data sets while performing feature selection. ER -