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The raw material of our paper is a well-known and commonly used type of supervised algorithms: decision trees. Using a training data, they provide some useful rules to classify new data sets. But a data set with missing values is always the bane of a data scientist. Even though decision tree algorithms such as ID3 and C4.5 (the two algorithms with which we are working in this paper) represent some of the simplest pattern classification algorithms that can be applied in many domains, but withdoi:10.14569/ijacsa.2018.091232 fatcat:mdcsp2clbvfhlcqsaw4mce6xqi