Bacterial foraging trained wavelet neural networks: application to bankruptcy prediction in banks
AbstractThis paper proposes a modified bacterial foraging technique (BFT) to train wavelet neural network (WNN) in order to predict bankruptcy in banks. The BFT is modified in that the swarming step that implements cell-to-cell interaction is deleted. The parameters translation, dilation and the weights connecting different layers in WNN are updated using the BFT. The resulting neural network is called BFTWNN. The effectiveness of BFTWNN is tested on bankruptcy prediction as well as benchmark datasets. We employed ten-fold cross validation in the study. Numerical experiments suggested that the BFTWNN outperformed threshold accepting trained wavelet neural network (TAWNN) (Vinaykumar et al., 2008) and WNN in benchmark datasets by wide margin while it yielded results comparable to that of differential evolution wavelet neural network (DEWNN) (Chauhan et al., 2009) in terms of area under the receiver-operating characteristic curve (AUC). Of particular significance is the superiority of the BFTWNN over the original WNN on all but one datasets.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Inderscience Enterprises Ltd in its journal Int. J. of Data Analysis Techniques and Strategies.
Volume (Year): 3 (2011)
Issue (Month): 3 ()
Contact details of provider:
Web page: http://www.inderscience.com/browse/index.php?journalID=282
wavelet neural networks; WNNs; bank bankruptcies; bankruptcy prediction; classification; bacterial foraging WNNs; BFTWNN; threshold accepting WNNs; TAWNN.;
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Graham Langley).
If references are entirely missing, you can add them using this form.