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In this paper, we propose a new feature evaluation method that forms the basis for feature ranking and selection. The method starts by generating a number of feature subsets in a random fashion and evaluates features based on the derived subsets. It then proceeds in a number of stages. In each stage, it inputs the features whose ranks in the previous stage were above the median rank and re-evaluates...
This paper presents application of machine learning ensembles, which randomly project the original high dimensional feature space onto multiple lower dimensional feature subspaces, to classification problems with high-dimensional feature spaces. The motivation is to address challenges associated with algorithm scalability, data sparsity and information loss due to the so-called curse of dimensionality...
Dimensionality reduction has long been an active research topic within statistics, pattern recognition, machine learning and data mining. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. In this paper, we transform the attribute selection problem into the optimization problem which...
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