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L. Buri, C. Hassan, G. Bersani, M. Anti, M. A. Bianco, L. Cipolletta, E. Di Giulio, G. Di Matteo, L. Familiari, L. Ficano, P. Loriga, S. Morini, V. Pietropaolo, A. Zambelli, E. Grossi, M. Intraligi, M. Buscema and SIED Appropriateness Working Group, “Appropriateness Guidelines and Predictive Rules to Select Patients for Upper Endoscopy: A Nationwide Multicenter Study,” American Journal of Gastroenterology, Vol. 105, No. 6, 2010, pp. 1327-1337. doi:10.1038/ajg.2009.675

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• TITLE: Training with Input Selection and Testing (TWIST) Algorithm: A Significant Advance in Pattern Recognition Performance of Machine Learning

  AUTHORS: Massimo Buscema, Marco Breda, Weldon Lodwick

  KEYWORDS: Neural Networks; Machine Learning; Pattern Recognition; Evolutionary Computation

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.5 No.1, February 22, 2013

  ABSTRACT: This article shows the efficacy of TWIST, a methodology for the design of training and testing data subsets extracted from given dataset associated with a problem to be solved via ANNs. The methodology we present is embedded in algorithms and actualized in computer software. Our methodology as implemented in software is compared to the current standard methods of random cross validation: 10-Fold CV, random split into two subsets and the more advanced T&T. For each strategy, 13 learning machines, representing different families of the main algorithms, have been trained and tested. All algorithms were implemented using the well-known WEKA software package. On one hand a falsification test with randomly distributed dependent variable has been used to show how T&T and TWIST behaves as the other two strategies: when there is no information available on the datasets they are equivalent. On the other hand, using the real Statlog (Heart) dataset, a strong difference in accuracy is experimentally proved. Our results show that TWIST is superior to current methods. Pairs of subsets with similar probability density functions are generated, without coding noise, according to an optimal strategy that extracts the most useful information for pattern classification.