Browsing by Author "Ting, Kai Ming"
Now showing items 1-5 of 6
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Boosting trees for cost-sensitive classifications
(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
(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
(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?
(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
(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 ...
