Now showing items 1-5 of 6

  • Boosting trees for cost-sensitive classifications

    Ting, Kai Ming; Zheng, Zijian (University of Waikato, Department of Computer Science, 1998-01)
    This paper explores two boosting techniques for cost-sensitive tree classification in the situation where misclassification costs change very often. Ideally, one would like to have only one induction, and use the induced ...
  • Inducing cost-sensitive trees via instance-weighting

    Ting, Kai Ming (Computer Science, University of Waikato, 1997-09)
    We introduce an instance-weighting method to induce cost-sensitive trees in this paper. It is a generalization of the standard tree induction process where only the initial instance weights determine the type of tree (i.e., ...
  • Learning from batched data: model combination vs data combination

    Ting, Kai Ming; Low, Boon Toh; Witten, Ian H. (Department of Computer Science, University of Waik, 1997-05)
    When presented with multiple batches of data, one can either combine them into a single batch before applying a machine learning procedure or learn from each batch independently and combine the resulting models. The former ...
  • Stacked generalization: when does it work?

    Ting, Kai Ming; Witten, Ian H. (Department of Computer Science, University of Waik, 1997-01)
    Stacked generalization is a general method of using a high-level model to combine lower-level models to achieve greater predictive accuracy. In this paper we resolve two crucial issues which have been considered to be a ...
  • Stacking bagged and dagged models

    Ting, Kai Ming; Witten, Ian H. (1997-03)
    In this paper, we investigate the method of stacked generalization in combining models derived from different subsets of a training dataset by a single learning algorithm, as well as different algorithms. The simplest way ...

Kai Ming Ting has 3 co-authors in Research Commons.