Nowadays huge amount of data is regularly being collected for various purposes by organizations and companies, including business, government departments, and medical service providers. Some of these techniques involve perturbing datasets by adding a noise to data in some controlled fashion. The effectiveness of such techniques is typically evaluated by measuring the security and data quality of perturbed dataset. In this paper the authors experimentally evaluate the data quality by comparing the prediction capability of decision trees and neural networks, built from original and perturbed datasets. The authors then compare this evaluation technique to the one that uses logic rules associated with the decision tree classifiers.
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