Uncertainty optimization based approach to data engineering

Data is generating at an exponential pace with the advance in information technology. Such data highly contain un- certain and vague information. Data engineering deals with the methodologies to assess and evaluate uncertainties in the dataset and generate useful information from the data pool. This work presents a mathematical approach to evaluate the dataset's uncer- tainties and its application to data reduction. The proposed method is used for attribute selection for early-predicting of diabetes. Experimental results show that the prediction accuracy using the rough set method is higher than the other methods.