Now showing items 1-3 of 3

  • A genetic algorithm approach to optimising random forests applied to class engineered data. 

    Elyan, Eyad; Gaber, Mohamed Medhat (Elsevier https://dx.doi.org/10.1016/j.ins.2016.08.007, 2016-08-04)
    ELYAN, E. and GABER, M.M. 2016. A genetic algorithm approach to optimising random forests applied to class engineered data. Information sciences [online], (accepted). Available from: https://dx.doi.org/10.1016/j.ins.2016.08.007
    In numerous applications and especially in the life science domain, examples are labelled at a higher level of granularity. For example, binary classification is dominant in many of these datasets, with the positive class ...
  • Incremental growth in modular neural networks. 

    Macleod, Christopher; Maxwell, Grant M.; Muthuraman, Sethuraman (Elsevier http://dx.doi.org/10.1016/j.engappai.2008.11.002, 2009-06)
    MACLEOD, C., MAXWELL, G. M., and MUTHURAMAN, S., 2009. Incremental growth in modular neural networks. Engineering Applications of Artificial Intelligence, 22 (4/5), pp. 660-666.
    This paper outlines an algorithm for incrementally growing Artificial Neural Networks. The algorithm allows the network to expand by adding new sub-networks or modules to an existing structure; the modules are trained using ...
  • The synthesis of artificial neural networks using single string evolutionary techniques. 

    Macleod, Christopher (The Robert Gordon University School of Electronic and Electrical Engineering, 1999)
    The research presented in this thesis is concerned with optimising the structure of Artificial Neural Networks. These techniques are based on computer modelling of biological evolution or foetal development. They are ...