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Variable selection is a significant pre-processing task for prediction in the field of data mining and machine learning, and involves the selection of a subset of relevant variables. Almost all researchers have faced the problem of missing data, which can occur due to nonresponse or loss of information. This thesis develops a new variable selection technique for dealing with missing data. Relief is an algorithm for estimating the quality of each variable and is applicable to categorical ordoi:10.7282/t3vd6wsb fatcat:hw62i62gaff2tchiy3tlcffkjq