Misclassification cost minimizing fitness functions for genetic algorithm-based artificial neural network classifiers

P Pendharkar ()
Additional contact information
P Pendharkar: Penn State University at Harrisburg

Journal of the Operational Research Society, 2009, vol. 60, issue 8, 1123-1134

Abstract: Abstract We study three different approaches to formulate a misclassification cost minimizing genetic algorithm (GA) fitness function for a GA-neural network classifier. These three different approaches include a fitness function that directly minimizes total misclassification cost, a fitness function that uses posterior probability for minimizing total misclassification cost and a hybrid fitness function that uses an average value of the first two fitness functions to minimize total misclassification cost. Using simulated data sets representing three different distributions and four different misclassification cost matrices, we test the performance of the three fitness functions on a two-group classification problem. Our results indicate that the posterior probability-based misclassification cost minimizing function and the hybrid fitness function are less prone to training data over fitting, but direct misclassification cost minimizing fitness function provides the lowest overall misclassification cost in training tests. For holdout sample tests, when cost asymmetries are low (less than or equal to a ratio of 1:2), the hybrid misclassification cost minimizing fitness function yields the best results; however, when cost asymmetries are high (equal or greater than a ratio of 1:4), the total misclassification cost minimizing function provides the best results. We validate our findings using a real-world data on a bankruptcy prediction problem.

Keywords: data mining; genetic algorithms; neural networks; misclassification cost; knowledge discovery; classification (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1057/palgrave.jors.2602641 Abstract (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:jorsoc:v:60:y:2009:i:8:d:10.1057_palgrave.jors.2602641

Ordering information: This journal article can be ordered from
http://www.springer. ... search/journal/41274

DOI: 10.1057/palgrave.jors.2602641

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald and Jonathan Crook

More articles in Journal of the Operational Research Society from Palgrave Macmillan, The OR Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().