Now showing items 2-4 of 4

  • A fine-grained random forests using class decomposition: an application to medical diagnosis. 

    Elyan, Eyad; Gaber, Mohamed Medhat (Springer http://dx.doi.org/10.1007/s00521-015-2064-z, 2015-09-22)
    ELYAN, E. and GABER, M.M. 2015. A fine-grained random forests using class decomposition: an application to medical diagnosis. Neural computing and applications [online], FirstOnline. Available from: http://dx.doi.org/10.1007/s00521-015-2064-z
    Class decomposition describes the process of segmenting each class into a number of homogeneous subclasses. This can be naturally achieved through clustering. Utilising class decomposition can provide a number of benefits ...
  • On pruning and feature engineering in Random Forests. 

    Fawagreh, Khaled (Robert Gordon University Faculty of Design and Technology, School of Computing Science and Digital Media, 2016-10-01)
    FAWAGREH, K. 2016. On pruning and feature engineering in Random Forests. Robert Gordon University, PhD thesis.
    Random Forest (RF) is an ensemble classification technique that was developed by Leo Breiman over a decade ago. Compared with other ensemble techniques, it has proved its accuracy and superiority. Many researchers, ...
  • Relation discovery from web data for competency management. 

    Zhu, Jianhan; Goncalves, Alexandre L.; Uren, Victoria; Motta, Enrico; Pacheco, Roberto; Eisenstadt, Marc; Song, Dawei (IOS Press, 2007-12)
    ZHU, J., GONCALVES, A. L., UREN, V. S., MOTTA, E., PACHECO, R., EISENSTADT, M. and SONG, D., 2007. Relation discovery from web data for competency management. Web Intelligence and Agent Systems, 5 (4), pp. 405-417.
    In current organizations, valuable enterprise knowledge is often buried under rapidly expanding huge amount of unstructured information in the form of web pages, blogs, and other forms of human text communications. ...