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A Novel Operational Partition between Neural Network Classifiers on Vulnerability to Data Mining Bias
2014
Journal of Software Engineering and Applications
It is difficult if not impossible to appropriately and effectively select from among the vast pool of existing neural network machine learning predictive models for industrial incorporation or academic research exploration and enhancement. When all models outperform all the others under disparate circumstances, none of the models do. Selecting the ideal model becomes a matter of illsupported opinion ungrounded on the extant real world environment. This paper proposes a novel grouping of the
doi:10.4236/jsea.2014.74027
fatcat:nyma6fa4wrcdnk7x6cb23fexmi