Now showing items 1-20 of 269

  • A 3-factor epistatic model predicts digital ulcers in Italian scleroderma patients

    Beretta, Lorenzo; Santaniello, Alessandro; Mayo, Michael; Cappiello, Francesca; Marchini, Maurizio; Scorza, Raffaella (Elsevier, 2010)
    Background The genetic background may predispose systemic sclerosis (SSc) patients to the development of digital ulcers (DUs). Methods Twenty-two functional cytokine single nucleotide polymorphisms (SNPs) and 3 HLA class ...
  • 3D face recognition using multiview keypoint matching

    Mayo, Michael; Zhang, Edmond Yiwen (IEEE, 2009)
    A novel algorithm for 3D face recognition based point cloud rotations, multiple projections, and voted keypoint matching is proposed and evaluated. The basic idea is to rotate each 3D point cloud representing an individual’s ...
  • Accelerating the XGBoost algorithm using GPU computing

    Mitchell, Rory; Frank, Eibe (2017)
    We present a CUDA-based implementation of a decision tree construction algorithm within the gradient boosting library XGBoost. The tree construction algorithm is executed entirely on the graphics processing unit (GPU) and ...
  • Accurate photometric redshift probability density estimation - method comparison and application

    Rau, Michael M.; Seitz, Stella; Frank, Eibe; Brimioulee, Fabrice; Friedrich, Oliver; Gruen, Daniel; Hoyle, Ben (Oxford University Press (OUP): Policy P - Oxford Open Option A, 2015)
    We introduce an ordinal classification algorithm for photometric redshift estimation, which significantly improves the reconstruction of photometric redshift probability density functions (PDFs) for individual galaxies and ...
  • Active learning of soft rules for system modelling

    Frank, Eibe; Huber, Klaus-Perter (1996)
    Using rule learning algorithms to model systems has gained considerable interest in the past. The underlying idea of active learning is to learning algorithm influence the selection of training examples. The presented ...
  • Adaptive feature thresholding for off-line signature verification

    Larkins, Robert L.; Mayo, Michael (IEEE Press, 2008)
    This paper introduces Adaptive Feature Thresholding (AFT) which is a novel method of person-dependent off-line signature verification. AFT enhances how a simple image feature of a signature is converted to a binary feature ...
  • An Adaptive Model-based Mutation Operator for the Wind Farm Layout Optimisation Problem

    Mayo, Michael; Daoud, Maisa (IEEE, 2015)
    A novel mutation operator for the wind farm layout optimisation problem is proposed and tested. When a wind farm layout is simulated, statistics such as an individual turbine’s wake free ratio can be computed. These ...
  • 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 ...
  • Additive Regression Applied to a Large-Scale Collaborative Filtering Problem

    Frank, Eibe; Hall, Mark A. (Springer, 2008)
    The much-publicized Netflix competition has put the spotlight on the application domain of collaborative filtering and has sparked interest in machine learning algorithms that can be applied to this sort of problem. The ...
  • Aesthetic local search of wind farm layouts

    Mayo, Michael; Daoud, Maisa (MDPI, 2017)
    The visual impact of wind farm layouts has seen little consideration in the literature on the wind farm layout optimisation problem to date. Most existing algorithms focus on optimising layouts for power or the cost of ...
  • 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 ...
  • Alternating model trees

    Frank, Eibe; Mayo, Michael; Kramer, Stefan (ACM Press, 2015)
    Model tree induction is a popular method for tackling regression problems requiring interpretable models. Model trees are decision trees with multiple linear regression models at the leaf nodes. In this paper, we propose ...
  • 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 ...
  • Annotate-Sample-Average (ASA): A New Distant Supervision Approach for Twitter Sentiment Analysis

    Bravo-Marquez, Felipe; Frank, Eibe; Pfahringer, Bernhard (IOS Press, 2016-01-01)
    The classification of tweets into polarity classes is a popular task in sentiment analysis. State-of-the-art solutions to this problem are based on supervised machine learning models trained from manually annotated examples. ...
  • The applicability of ambient sensors as proximity evidence for NFC transactions

    Shepherd, Carlton; Gurulian, Iakovos; Frank, Eibe; Markantonakis, Konstantinos; Akram, Raja Naeem; Panaousis, Emmanouil; Mayes, Keith (IEEE Computer Society, 2017)
    Near Field Communication (NFC) has enabled mobile phones to emulate contactless smart cards. Similar to contactless smart cards, they are also susceptible to relay attacks. To counter these, a number of methods have been ...
  • 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, ...
  • Applying machine learning to agricultural data

    McQueen, Robert J.; Garner, Stephen R.; Nevill-Manning, Craig G.; Witten, Ian H. (1994-07)
    Many techniques have been developed for abstracting, or "learning," rules and relationships from diverse data sets, in the hope that machines can help in the often tedious and error-prone process of acquiring knowledge ...
  • Applying propositional learning algorithms to multi-instance data

    Frank, Eibe; Xu, Xin (University of Waikato, Department of Computer Science, 2003-06)
    Multi-instance learning is commonly tackled using special-purpose algorithms. Development of these algorithms has started because early experiments with standard propositional learners have failed to produce satisfactory ...
  • Artificial neural network is highly predictive of outcome in paediatric acute liver failure

    Rajanayagam, J.; Frank, Eibe; Shepherd, R. W.; Lewindon, P. J. (Wiley, 2013)
    Current prognostic models in PALF are unreliable, failing to account for complex, non-linear relationships existing between multiple prognostic factors. A computational approach using ANN should provide superior modelling ...
  • Automatic species identification of live moths

    Mayo, Michael; Watson, Anna T. (Elsevier Science Publishers B.V., 2007)
    A collection consisting of the images of 774 live moth individuals, each moth belonging to one of 35 different UK species, was analysed to determine if data mining techniques could be used effectively for automatic species ...