Browsing by Author "Trigg, Leonard E."

Now showing items 1-5 of 7

  • Data mining in bioinformatics using Weka

    Frank, Eibe; Hall, Mark A.; Trigg, Leonard E.; Holmes, Geoffrey; Witten, Ian H. (Oxford University Press., 2004)
    The Weka machine learning workbench provides a general purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an ...
  • A diagnostic tool for tree based supervised classification learning algorithms

    Holmes, Geoffrey; Trigg, Leonard E. (1999-03)
    The process of developing applications of machine learning and data mining that employ supervised classification algorithms includes the important step of knowledge verification. Interpretable output is presented to a user ...
  • An entropy gain measure of numeric prediction performance

    Trigg, Leonard E. (University of Waikato, Department of Computer Science, 1998-05)
    Categorical classifier performance is typically evaluated with respect to error rate, expressed as a percentage of test instances that were not correctly classified. When a classifier produces multiple classifications for ...
  • Experiences with a weighted decision tree learner

    Cleary, John G.; Trigg, Leonard E. (University of Waikato, Department of Computer Science, 1998-05)
    Machine learning algorithms for inferring decision trees typically choose a single “best” tree to describe the training data. Recent research has shown that classification performance can be significantly improved by voting ...
  • Jumble Java Byte Code to Measure the Effectiveness of Unit Tests

    Irvine, Sean A.; Pavlinic, Tin; Trigg, Leonard E.; Cleary, John G.; Inglis, Stuart J.; Utting, Mark (IEEE Computer Society, 2007)
    Jumble is a byte code level mutation testing tool for Java which inter-operates with JUnit. It has been designed to operate in an industrial setting with large projects. Heuristics have been included to speed the checking ...