Computational geometry: an introduction
Computational geometry: an introduction
Computing the convex hull of a simple polygon
Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Uniform object generation for optimizing one-class classifiers
The Journal of Machine Learning Research
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Soft clustering using weighted one-class support vector machines
Pattern Recognition
Minimum spanning tree based one-class classifier
Neurocomputing
A novelty detection approach to classification
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Approximate convex hulls family for one-class classification
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Semi-supervised classification based on random subspace dimensionality reduction
Pattern Recognition
Sorted random projections for robust rotation-invariant texture classification
Pattern Recognition
Random projection, margins, kernels, and feature-selection
SLSFS'05 Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection
SMI 2012: Full GPU accelerated convex hull computation
Computers and Graphics
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In this work, a new one-class classification ensemble strategy called approximate polytope ensemble is presented. The main contribution of the paper is threefold. First, the geometrical concept of convex hull is used to define the boundary of the target class defining the problem. Expansions and contractions of this geometrical structure are introduced in order to avoid over-fitting. Second, the decision whether a point belongs to the convex hull model in high dimensional spaces is approximated by means of random projections and an ensemble decision process. Finally, a tiling strategy is proposed in order to model non-convex structures. Experimental results show that the proposed strategy is significantly better than state of the art one-class classification methods on over 200 datasets.