Steven De Bruyne

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We consider the linear classification method consisting of separating two sets of points in d-space by a hyperplane. We wish to determine the hyperplane which minimises the sum of distances from all misclassified points to the hyperplane. To this end two local descent methods are developed, one grid-based and one optimisation-theory based, and are embedded(More)
We investigate the effects of dimensionality reduction using different techniques and different dimensions on six two-class data sets with numerical attributes as pre-processing for two classification algorithms. Besides reducing the dimensionality with the use of principal components and linear discriminants, we also introduce four new techniques. After(More)
In [3] it has been demonstrated that decision trees built in a feature space yielded by some eigen transformation can be competitive with industry standards. Unfortunately, the selection of such a transformation and the dimension of the feature space that should be retained is not self-evident. These trees however have interesting properties that can be(More)
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