Developing an effective validation strategy for genetic programming models based on multiple datasets

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  title =        "Developing an effective validation strategy for
                 genetic programming models based on multiple datasets",
  author =       "Yi Liu and Taghi M. Khoshgoftaar and Jenq-Foung Yao",
  year =         "2006",
  booktitle =    "2006 IEEE International Conference on Information
                 Reuse and Integration",
  pages =        "232--237",
  address =      "Waikoloa Village, HI, USA",
  month =        sep,
  publisher =    "IEEE",
  bibdate =      "2006-11-14",
  bibsource =    "DBLP,
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/IRI.2006.252418",
  abstract =     "Genetic programming (GP) is a parallel searching
                 technique where many solutions can be obtained
                 simultaneously in the searching process. However, when
                 applied to real-world classification tasks, some of the
                 obtained solutions may have poor predictive
                 performances. One of the reasons is that these
                 solutions only match the shape of the training dataset,
                 failing to learn and generalise the patterns hidden in
                 the dataset. Therefore, unexpected poor results are
                 obtained when the solutions are applied to the test
                 dataset. This paper addresses how to remove the
                 solutions which will have unacceptable performances on
                 the test dataset. The proposed method in this paper
                 applies a multi-dataset validation phase as a filter in
                 GP-based classification tasks. By comparing our
                 proposed method with a standard GP classifier based on
                 the datasets from seven different NASA software
                 projects, we demonstrate that the multi-dataset
                 validation is effective, and can significantly improve
                 the performance of GP-based software quality
                 classification models",
  notes =        "",

Genetic Programming entries for Yi Liu Taghi M Khoshgoftaar Jenq-Foung Yao