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Towards a framework for multiple artificial neural network topologies validation by means of statistics
Author(s) -
GonzalezCarrasco Israel,
GarciaCrespo Angel,
RuizMezcua Belen,
LopezCuadrado Jose Luis,
ColomoPalacios Ricardo
Publication year - 2014
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2012.00653.x
Subject(s) - computer science , randomness , artificial neural network , machine learning , artificial intelligence , classifier (uml) , relevance (law) , statistical hypothesis testing , network topology , range (aeronautics) , data mining , statistics , materials science , mathematics , political science , law , composite material , operating system
Artificial neural networks (ANNs) are flexible computing tools that have been applied to a wide range of domains with a notable level of accuracy. However, there are multiple choices of ANNs classifiers in the literature that produce dissimilar results. As a consequence of this, the selection of this classifier is crucial for the overall performance of the system. In this work, an integral framework is proposed for the optimization of different ANN classifiers based on statistical hypothesis testing. The framework is tested in a real ballistic scenario. The new quality measures introduced, based on the Student t‐test, and employed throughout the framework, ensure the validity of results from a statistical standpoint; they reduce the appearance of experimental errors or the appearance of possible randomness. Results show the relevance of this framework, proving that its application improves the performance and efficiency of multiple classifiers.