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Fahlman, S. and Lebiere, C., (1990) The Cascade-Correlation Learning Architecture. In: Duda, R., Hart, P. and Stork, D., Eds., Advances in Neural Information Processing Systems 2, Morgan Kaufmann, Pattern Classification, Wiley-Interscience, Hoboken, 524-532.

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• TITLE: A Novel Operational Partition between Neural Network Classifiers on Vulnerability to Data Mining Bias

  AUTHORS: Charles Wong

  KEYWORDS: Machine Learning, Neural Networks, Data Mining, Data Dredging, Non-Stationary Time Series Analysis, Permanent Data Learning, Reversible Data Learning

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.7 No.4, April 17, 2014

  ABSTRACT: 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 ill-supported opinion ungrounded on the extant real world environment. This paper proposes a novel grouping of the model pool grounded along a non-stationary real world data line into two groups: Permanent Data Learning and Reversible Data Learning. This paper further proposes a novel approach towards qualitatively and quantitatively demonstrating their significant differences based on how they alternatively approach dynamic and raw real world data vs static and prescient data mining biased laboratory data. The results across 2040 separate simulation runs using 15,600 data points in realistically operationally controlled data environments show that the two-group division is effective and significant with clear qualitative, quantitative and theoretical support. Results across the empirical and theoretical spectrum are internally and externally consistent yet demonstrative of why and how this result is non-obvious.