Abstract
A novel machine learning paradigm, i.e., enclosing machine learning based on regular geometric shapes was proposed. First, it adopted regular minimum volume enclosing and bounding geometric shapes (sphere, ellipsoid, box) or their unions and so on to obtain one class description model. Second, Data description, two class classification, learning algorithms based on the one class description model were presented. The most obvious feature was that enclosing machine learning emphasized one class description and learning. To illustrate the concepts and algorithms, a minimum volume enclosing ellipsoid (MVEE) case for enclosing machine learning was then investigated in detail. Implementation algorithms for enclosing machine learning based on MVEE were presented. Subsequently, we validate the performances of MVEE learners using real world datasets. For novelty detection, a benchmark ball bearing dataset is adopted. For pattern classification, a benchmark iris dataset is investigated. The performance results show that our proposed method is comparable even better than Support Vector Machines (SVMs) in the datasets studied.
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Acknowledgments
This paper is jointly supported by NSFC and CAAC under grant #60672179, and also supported by the Doctorate Foundation of the Engineering College, Air Force Engineering University of China under grant #BC0501.
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Wei, XK., Li, YH., Li, YF. et al. Enclosing machine learning: concepts and algorithms. Neural Comput & Applic 17, 237–243 (2008). https://doi.org/10.1007/s00521-007-0113-y
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DOI: https://doi.org/10.1007/s00521-007-0113-y