Browsing by Author "Holmes, Geoffrey"

Now showing items 1-5 of 105

  • Adaptive random forests for evolving data stream classification

    Gomes, Heitor Murilo; Bifet, Albert; Read, Jesse; Barddal, Jean Paul; Enembreck, Fabrício; Pfahringer, Bernhard; Holmes, Geoffrey; Abdessalem, Talel (Springer, 2017)
    Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high learning performance and low demands with respect to input ...
  • Algorithm selection on data streams

    van Rijn, Jan N.; Holmes, Geoffrey; Pfahringer, Bernhard; Vanschoren, Joaquin (Springer International Publishing, 2014)
    We explore the possibilities of meta-learning on data streams, in particular algorithm selection. In a first experiment we calculate the characteristics of a small sample of a data stream, and try to predict which classifier ...
  • Analysing chromatographic data using data mining to monitor petroleum content in water

    Holmes, Geoffrey; Fletcher, Dale; Reutemann, Peter; Frank, Eibe (Springer, 2009)
    Chromatography is an important analytical technique that has widespread use in environmental applications. A typical application is the monitoring of water samples to determine if they contain petroleum. These tests are ...
  • An application of data mining to fruit and vegetable sample identification using Gas Chromatography-Mass Spectrometry

    Holmes, Geoffrey; Fletcher, Dale; Reutemann, Peter (iEMSs, 2012)
    One of the uses of Gas Chromatography-Mass Spectrometry (GC-MS) is in the detection of pesticide residues in fruit and vegetables. In a high throughput laboratory there is the potential for sample swaps or mislabelling, ...
  • Batch-Incremental Learning for Mining Data Streams

    Holmes, Geoffrey; Kirkby, Richard Brendon; Bainbridge, David (University of Waikato, 2004)
    The data stream model for data mining places harsh restrictions on a learning algorithm. First, a model must be induced incrementally. Second, processing time for instances must keep up with their speed of arrival. Third, ...