Failure prediction of dotcom companies using neural network-genetic programming hybrids

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "P. Ravisankar and V. Ravi and I. Bose",
  title =        "Failure prediction of dotcom companies using neural
                 network-genetic programming hybrids",
  journal =      "Information Sciences",
  volume =       "180",
  number =       "8",
  pages =        "1257--1267",
  year =         "2010",
  ISSN =         "0020-0255",
  DOI =          "doi:10.1016/j.ins.2009.12.022",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Dotcom
                 companies, Failure prediction, Feature selection,
                 Multilayer feed forward neural network, Probabilistic
                 neural network, t-Statistic, f-Statistic",
  abstract =     "This paper presents novel neural network-genetic
                 programming hybrids to predict the failure of dotcom
                 companies. These hybrids comprise multi-layer feed
                 forward neural network (MLFF), probabilistic neural
                 network (PNN), rough sets (RS) and genetic programming
                 (GP) in a two-phase architecture. In each hybrid, one
                 technique is used to perform feature selection in the
                 first phase and another one is used as a classifier in
                 the second phase. Further t-statistic and f-statistic
                 are also used separately for feature selection in the
                 first phase. In each of these cases, top 10 features
                 are selected and fed to the classifier. Also, the NN-GP
                 hybrids are compared with MLFF, PNN and GP in their
                 stand-alone mode without feature selection. The dataset
                 analyzed here is collected from Wharton Research Data
                 Services (WRDS). It consists of 240 dotcom companies of
                 which 120 are failed and 120 are healthy. Ten-fold
                 cross-validation is performed throughout the study.
                 Results in terms of average accuracy, average
                 sensitivity, average specificity and area under the
                 receiver operating characteristic curve (AUC) indicate
                 that the GP outperformed all the techniques with or
                 without feature selection. The superiority of GP-GP is
                 demonstrated by t-test at 10percent level of
                 significance. Furthermore, the results are much better
                 than those reported in previous studies on the same

Genetic Programming entries for P Ravi Shankar Vadlamani Ravi Indranil Bose