• Adaptive random forests for evolving data stream classification 

  Gomes, Heitor M.; 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, ...

  

• Reconstructing Data Provenance from Log Files 

  Tan, Yu Shyang (The University of Waikato, 2017)

  Data provenance describes the derivation history of data, capturing details such as the entities involved and the relationships between entities. Knowledge of data provenance can be used to address issues, such as data ...

  

• Distributed Operating Systems on Wireless Sensor Networks 

  Hunkin, Paul Wade (University of Waikato, 2017)

  This thesis proposes the use of traditional distributed operating system and distributed systems techniques that are adapted and applied to the wireless sensor network domain. These techniques are applied to the creation ...

  

• Scalable Text Mining with Sparse Generative Models 

  Puurula, Antti (University of Waikato, 2015)

  The information age has brought a deluge of data. Much of this is in text form, insurmountable in scope for humans and incomprehensible in structure for computers. Text mining is an expanding field of research that seeks ...

  

• Parameter Tuning Using Gaussian Processes 

  Ma, Jinjin (University of Waikato, 2012)

  Most machine learning algorithms require us to set up their parameter values before applying these algorithms to solve problems. Appropriate parameter settings will bring good performance while inappropriate parameter ...

  

• Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation 

  Mutter, Stefan (University of Waikato, 2011)

  Detecting similarity in biological sequences is a key element to understanding the mechanisms of life. Researchers infer potential structural, functional or evolutionary relationships from similarity. However, the concept ...

  

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