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'Hubness' is a recently discovered general problem of machine learning in high dimensional data spaces. Hub objects have a small distance to an exceptionally large number of data points, and anti-hubs are far from all other data points. It is related to the concentration of distances which impairs the contrast of distances in high dimensional spaces. Computation of secondary distances inspired by...
This paper presents a simulation-based empirical study of the performance profile of random sub sample ensembles with a hybrid mix of base learner composition in high dimensional feature spaces. The performance of hybrid random sub sample ensemble that uses a combination of C4.5, k-nearest neighbor (kNN) and naïve Bayes base learners is assessed through statistical testing in comparison to those...
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 the comparison of three subsampling techniques for random subspace ensemble classifiers through an empirical study. A version of random subspace ensemble designed to address the challenges of high dimensional classification, entitled random subsample ensemble, within the voting combiner framework was evaluated for its performance for three different sampling methods which entailed...
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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