Evaluation of Estimation Models using the Minimum Interval of Equivalence

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@Article{Dolado:2016:ASOC,
  author =       "Jose Javier Dolado and Daniel Rodriguez and 
                 Mark Harman and William B. Langdon and Federica Sarro",
  title =        "Evaluation of Estimation Models using the Minimum
                 Interval of Equivalence",
  journal =      "Applied Soft Computing",
  year =         "2016",
  volume =       "49",
  pages =        "956--967",
  month =        dec,
  note =         "in press",
  keywords =     "genetic algorithms, genetic programming, Software
                 estimations, Soft computing, Equivalence Hypothesis
                 Testing, Credible intervals, Bootstrap",
  URL =          "http://www.sciencedirect.com/science/article/pii/S1568494616301557",
  DOI =          "doi:10.1016/j.asoc.2016.03.026",
  ISSN =         "1568-4946",
  size =         "42 pages",
  abstract =     "a new measure to compare soft computing methods for
                 software estimation. This new measure is based on the
                 concepts of Equivalence Hypothesis Testing (EHT). Using
                 the ideas of EHT, a dimensionless measure is defined
                 using the Minimum Interval of Equivalence and a random
                 estimation. The dimensionless nature of the metric
                 allows us to compare methods independently of the data
                 samples used. The motivation of the current proposal
                 comes from the biases that other criteria show when
                 applied to the comparison of software estimation
                 methods. In this work, the level of error for comparing
                 the equivalence of methods is set using EHT. Several
                 soft computing methods are compared, including genetic
                 programming, neural networks, regression and model
                 trees, linear regression (ordinary and least mean
                 squares) and instance-based methods. The experimental
                 work has been performed on several publicly available
                 datasets.

                 Given a dataset and an estimation method we compute the
                 upper point of Minimum Interval of Equivalence, MIEu,
                 on the confidence intervals of the errors. Afterwards,
                 the new measure, MIEratio, is calculated as the
                 relative distance of the MIEu to the random
                 estimation.

                 Finally, the data distributions of the MIEratios are
                 analysed by means of probability intervals, showing the
                 viability of this approach. In this experimental work,
                 it can be observed that there is an advantage for the
                 genetic programming and linear regression methods by
                 comparing the values of the intervals.",
}

Genetic Programming entries for Jose Javier Dolado Cosin Daniel Rodriguez Mark Harman William B Langdon Federica Sarro

Citations