Title :
Avoiding Pitfalls in Neural Network Research
Author_Institution :
J. Mack Robinson Coll. of Bus., Georgia State Univ., Atlanta, GA
Abstract :
Artificial neural networks (ANNs) have gained extensive popularity in recent years. Research activities are considerable, and the literature is growing. Yet, there is a large amount of concern on the appropriate use of neural networks in published research. The purposes of this paper are to: 1) point out common pitfalls and misuses in the neural network research; 2) draw attention to relevant literature on important issues; and 3) suggest possible remedies and guidelines for practical applications. The main message we aim to deliver is that great care must be taken in using ANNs for research and data analysis
Keywords :
data analysis; neural nets; pattern classification; artificial neural network research; data analysis; pitfalls avoidance; Application software; Data analysis; Databases; Guidelines; Humans; Neural networks; Pattern recognition; Predictive models; Software packages; Statistics; Data; model building; model evaluation; neural networks; pitfalls; publication bias; software;
Journal_Title :
Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on
DOI :
10.1109/TSMCC.2006.876059