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Combining Neural Networks and Statistical Predictions to Solve the Classification Problem in Discriminant Analysis *
Author(s) -
Markham Ina S.,
Ragsdale Cliff T.
Publication year - 1995
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1995.tb01427.x
Subject(s) - linear discriminant analysis , computer science , artificial neural network , artificial intelligence , discriminant , machine learning , set (abstract data type) , statistical analysis , variety (cybernetics) , optimal discriminant analysis , data mining , mathematics , statistics , programming language
A number of recent studies have compared the performance of neural networks (NNs) to a variety of statistical techniques for the classification problem in discriminant analysis. The empirical results of these comparative studies indicate that while NNs often outperform the more traditional statistical approaches to classification, this is not always the case. Thus, decision makers interested in solving classification problems are left in a quandary as to what tool to use on a particular data set. We present a new approach to solving classification problems by combining the predictions of a well‐known statistical tool with those of an NN to create composite predictions that are more accurate than either of the individual techniques used in isolation.