Building Decision Tree Software Quality Classification Models Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{liu2:2003:gecco,
  author =       "Yi Liu and Taghi M. Khoshgoftaar",
  title =        "Building Decision Tree Software Quality Classification
                 Models Using Genetic Programming",
  booktitle =    "Genetic and Evolutionary Computation -- GECCO-2003",
  editor =       "E. Cant{\'u}-Paz and J. A. Foster and K. Deb and 
                 D. Davis and R. Roy and U.-M. O'Reilly and H.-G. Beyer and 
                 R. Standish and G. Kendall and S. Wilson and 
                 M. Harman and J. Wegener and D. Dasgupta and M. A. Potter and 
                 A. C. Schultz and K. Dowsland and N. Jonoska and 
                 J. Miller",
  year =         "2003",
  pages =        "1808--1809",
  address =      "Chicago",
  publisher_address = "Berlin",
  month =        "12-16 " # jul,
  volume =       "2724",
  series =       "LNCS",
  ISBN =         "3-540-40603-4",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming, poster",
  DOI =          "doi:10.1007/3-540-45110-2_75",
  abstract =     "Predicting the quality of software modules prior to
                 testing or system operations allows a focused software
                 quality improvement endeavor. Decision trees are very
                 attractive for classification problems, because of
                 their comprehensibility and white box modeling
                 features. However, optimizing the classification
                 accuracy and the tree size is a difficult problem, and
                 to our knowledge very few studies have addressed the
                 issue. This paper presents an automated and simplified
                 genetic programming (GP) based decision tree modeling
                 technique for calibrating software quality
                 classification models. The proposed technique is based
                 on multi-objective optimization using strongly typed
                 GP. Two fitness functions are used to optimize the
                 classification accuracy and tree size of the
                 classification models calibrated for a real-world
                 high-assurance software system. The performances of the
                 classification models are compared with those obtained
                 by standard GP. It is shown that the GP-based decision
                 tree technique yielded better classification models.",
  notes =        "GECCO-2003. A joint meeting of the twelfth
                 International Conference on Genetic Algorithms
                 (ICGA-2003) and the eighth Annual Genetic Programming
                 Conference (GP-2003)",
}

Genetic Programming entries for Yi Liu Taghi M Khoshgoftaar

Citations