Modeling Sparse Engine Test Data Using Genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Tina Yu and Jim Rutherford",
  title =        "Modeling Sparse Engine Test Data Using Genetic
  booktitle =    "The Seventh ACM SIGKDD International Conference on
                 Knowledge Discovery and Data Mining",
  year =         "2001",
  address =      "San Francisco, California, USA",
  month =        "26-29 " # aug,
  keywords =     "genetic algorithms, genetic programming, Data
                 Modeling, Sparse Data, High Dimensionality, Virtual
  URL =          "",
  URL =          "",
  abstract =     "We demonstrate the generation of an engine test model
                 using Genetic Programming. In particular, a two-phase
                 modeling process is proposed to handle the
                 high-dimensionality and sparseness natures of the
                 engine test data. The resulting model gives high
                 accuracy prediction on training data. It is also very
                 good in predicting low range data values. However, at
                 least partly due to limitations of the data set, its
                 accuracy on validation data and high range data values
                 is not satisfactory. Moreover, the subject experts
                 could not interpret its real-world meaning. We hope the
                 results of this study can benefit other engine oil
                 modeling applications.",

Genetic Programming entries for Tina Yu Jim Rutherford