Now showing items 1-36 of 36

  • Informed mutation of wind farm layouts to maximise energy harvest

    Mayo, Michael; Daoud, Maisa (Elsevier, 2016)
    Correct placement of turbines in a wind farm is a critical issue in wind farm design optimisation. While traditional "trial and error"-based approaches suffice for small layouts, automated approaches are required for larger ...
  • Deferral classification of evolving temporal dependent data streams

    Mayo, Michael; Bifet, Albert (ACM, 2016)
    Data streams generated in real-time can be strongly temporally dependent. In this case, standard techniques where we suppose that class labels are not correlated may produce sub-optimal performance because the assumption ...
  • Towards a new evolutionary subsampling technique for heuristic optimisation of load disaggregators

    Mayo, Michael; Omranian, Sara (Springer, 2016)
    In this paper we present some preliminary work towards the development of a new evolutionary subsampling technique for solving the non-intrusive load monitoring (NILM) problem. The NILM problem concerns using predictive ...
  • 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 ...
  • 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 ...
  • Evolving artificial datasets to improve interpretable classifiers

    Mayo, Michael; Sun, Quan (IEEE, 2014)
    Differential Evolution can be used to construct effective and compact artificial training datasets for machine learning algorithms. In this paper, a series of comparative experiments are performed in which two simple ...
  • Predicting regression test failures using genetic algorithm-selected dynamic performance analysis metrics

    Mayo, Michael; Spacey, Simon (Springer, 2013)
    A novel framework for predicting regression test failures is proposed. The basic principle embodied in the framework is to use performance analysis tools to capture the runtime behaviour of a program as it executes each ...
  • Towards a framework for designing full model selection and optimization systems

    Sun, Quan; Pfahringer, Bernhard; Mayo, Michael (Springer, 2013)
    People from a variety of industrial domains are beginning to realise that appropriate use of machine learning techniques for their data mining projects could bring great benefits. End-users now have to face the new problem ...
  • Identifying Market Price Levels Using Differential Evolution

    Mayo, Michael (Springer Berlin Heidelberg, 2013)
    Evolutionary data mining is used in this paper to investigate the concept of support and resistance levels in financial markets. Specifically, Differential Evolution is used to learn support/resistance levels from price ...
  • Cartesian genetic programming for trading: a preliminary investigation

    Mayo, Michael (Australian Computer Society, Inc., 2012)
    In this paper, a preliminary investigation of Cartesian Genetic Programming (CGP) for algorithmic intraday trading is conducted. CGP is a recent new variant of genetic programming that differs from traditional approaches ...
  • Full model selection in the space of data mining operators

    Sun, Quan; Pfahringer, Bernhard; Mayo, Michael (ACM, 2012)
    We propose a framework and a novel algorithm for the full model selection (FMS) problem. The proposed algorithm, combining both genetic algorithms (GA) and particle swarm optimization (PSO), is named GPS (which stands for ...
  • Evolutionary data selection for enhancing models of intraday forex time series

    Mayo, Michael (2012)
    The hypothesis in this paper is that a significant amount of intraday market data is either noise or redundant, and that if it is eliminated, then predictive models built using the remaining intraday data will be more ...
  • Experiments with multi-view multi-instance learning for supervised image classification

    Mayo, Michael; Frank, Eibe (2011)
    In this paper we empirically investigate the benefits of multi-view multi-instance (MVMI) learning for supervised image classification. In multi-instance learning, examples for learning contain bags of feature vectors and ...
  • Hybridizing data stream mining and technical indicators in automated trading systems

    Mayo, Michael (Springer-Verlag Berlin Heidelberg, 2011)
    Automated trading systems for financial markets can use data mining techniques for future price movement prediction. However, classifier accuracy is only one important component in such a system: the other is a decision ...
  • Modelling epistasis in genetic disease using Petri nets, evolutionary computation and frequent itemset mining

    Mayo, Michael; Beretta, Lorenzo (Elsevier, 2010)
    Petri nets are useful for mathematically modelling disease-causing genetic epistasis. A Petri net model of an interaction has the potential to lead to biological insight into the cause of a genetic disease. However, defining ...
  • Enhanced spatial pyramid matching using log-polar-based image subdivision and representation

    Zhang, Edmond Yiwen; Mayo, Michael (IEEE, 2010)
    This paper presents a new model for capturing spatial information for object categorization with bag-of-words (BOW). BOW models have recently become popular for the task of object recognition, owing to their good performance ...
  • Improving Bag-of-Words model with spatial information

    Zhang, Edmond Yiwen; Mayo, Michael (IEEE, 2010)
    Bag-of-Words (BOW) models have recently become popular for the task of object recognition, owing to their good performance and simplicity. Much work has been proposed over the years to improve the BOW model, where the ...
  • Evolving concurrent Petri net models of epistasis

    Mayo, Michael; Beretta, Lorenzo (2010)
    A genetic algorithm is used to learn a non-deterministic Petri netbased model of non-linear gene interactions, or statistical epistasis. Petri nets are computational models of concurrent processes. However, often certain ...
  • 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 ...
  • SIFTing the relevant from the irrelevant: Automatically detecting objects in training images

    Zhang, Edmond Yiwen; Mayo, Michael (2009)
    Many state-of-the-art object recognition systems rely on identifying the location of objects in images, in order to better learn its visual attributes. In this paper, we propose four simple yet powerful hybrid ROI detection ...
  • Pattern discovery for object categorization

    Zhang, Edmond Yiwen; Mayo, Michael (2008)
    This paper presents a new approach for the object categorization problem. Our model is based on the successful `bag of words' approach. However, unlike the original model, image features (keypoints) are not seen as independent ...
  • Adaptive feature thresholding for off-line signature verification

    Larkins, Robert L.; Mayo, Michael (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 ...
  • Improving face gender classification by adding deliberately misaligned faces to the training data

    Mayo, Michael; Zhang, Edmond Yiwen (2008)
    A novel method of face gender classifier construction is proposed and evaluated. Previously, researchers have assumed that a computationally expensive face alignment step (in which the face image is transformed so that ...
  • Effective classifiers for detecting objects

    Mayo, Michael (2007)
    Several state-of-the-art machine learning classifiers are compared for the purposes of object detection in complex images, using global image features derived from the Ohta color space and Local Binary Patterns. Image ...
  • 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 ...
  • Random convolution ensembles

    Mayo, Michael (Springer, 2007)
    A novel method for creating diverse ensembles of image classifiers is proposed. The idea is that, for each base image classifier in the ensemble, a random image transformation is generated and applied to all of the images ...
  • A novel two stage scheme utilizing the test set for model selection in text classification

    Pfahringer, Bernhard; Reutemann, Peter; Mayo, Michael (2005)
    Text classification is a natural application domain for semi-supervised learning, as labeling documents is expensive, but on the other hand usually an abundance of unlabeled documents is available. We describe a novel ...
  • Using 2D and 3D landmarks to solve the correspondence problem in cognitive robot mapping

    Jefferies, Margaret E.; Cree, Michael J.; Mayo, Michael; Baker, Jesse T. (Springer Berlin, 2005)
    We present an approach which uses 2D and 3D landmarks for solving the correspondence problem in Simultaneous Localisation and Mapping (SLAM) in cognitive robot mapping. The nodes in the topological map are a representation ...
  • Bayesian sequence learning for predicting protein cleavage points

    Mayo, Michael (Springer, 2005)
    A challenging problem in data mining is the application of efficient techniques to automatically annotate the vast databases of biological sequence data. This paper describes one such application in this area, to the ...
  • Learning Petri net models of non-linear gene interactions

    Mayo, Michael (Elsevier Science Publishers B.V., 2005)
    Understanding how an individual's genetic make-up influences their risk of disease is a problem of paramount importance. Although machine-learning techniques are able to uncover the relationships between genotype and ...
  • Using context to solve the correspondence problem in Simultaneous Localisation and Mapping

    Jefferies, Margaret E.; Weng, Wenrong; Baker, Jesse T.; Mayo, Michael (Springer Berlin, 2004)
    We present a method for solving the correspondence problem in Simultaneous Localisation and Mapping (SLAM) in a topological map. The nodes in the topological map are a representation for each local space the robot visits. ...
  • A multi-player educational game for story writing

    Mayo, Michael (2004)
    In this short paper, a multi-player interactive game called STORYWORLD BUILDER is described. The game enables children to collaboratively build a virtual “story world” and then role-play characters in that world. The ...
  • Two computer-based learning environments for reading and writing narratives

    Mayo, Michael (2004)
    In this brief paper, two computer-based educational tools are described. They are designed to support children learning the literacy skills of narrative comprehension and creation. We give an overview of these tools, and ...
  • Symbol grounding and its implications for artificial intelligence

    Mayo, Michael (Australia: Australian Computer Society, Inc. Darlinghurst, Australia, 2003)
    In response to Searle's well-known Chinese room argument against Strong AI (and more generally, computationalism), Harnad proposed that if the symbols manipulated by a robot were sufficiently grounded in the real world, ...
  • Optimising ITS behaviour with Bayesian networks and decision theory

    Mayo, Michael; Mitrovic, Antonija (2001)
    We propose and demonstrate a methodology for building tractable normative intelligent tutoring systems (ITSs). A normative ITS uses a Bayesian network for long-term student modelling and decision theory to select the next ...

Showing up to 5 theses - most recently added to Research Commons first.

  • Linear Genetic Programming with Experience

    Liu, Liang (University of Waikato, 2015)
    A novel method of using Machine Learning (ML) algorithms to improve the performance of Linear Genetic Programming (LGP) is studied. In this study, structures used to organize the trained ML models are called Experience ...
  • Meta-Learning and the Full Model Selection Problem

    Sun, Quan (University of Waikato, 2014)
    When working as a data analyst, one of my daily tasks is to select appropriate tools from a set of existing data analysis techniques in my toolbox, including data preprocessing, outlier detection, feature selection, learning ...
  • Improving the Evaluation of Network Anomaly Detection Using a Data Fusion Approach

    Löf, Andreas (University of Waikato, 2013)
    Currently, the evaluation of network anomaly detection methods is often not repeatable. It is difficult to ascertain if different implementations of the same methods have the same performance or the relative performance ...
  • Improving Bags-of-Words model for object categorization

    Zhang, Edmond Yiwen (University of Waikato, 2013)
    In the past decade, Bags-of-Words (BOW) models have become popular for the task of object recognition, owing to their good performance and simplicity. Some of the most effective recent methods for computer-based object ...
  • 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 ...