This paper proposes a novel classification paradigm in which the properties of the Order Statistics (OS) have been used to perform an optimal/near-optimal solution for multi-dimensional problems. In our initial works in [5] and [6], we proposed the foundational theory of CMOS, Classification by the Moments of Order Statistics, for some uni-dimensional symmetric and asymmetric distributions of the exponential family. In this paper, we generalize those results for various multidimensional distributions. The strategy is analogous to a Naïve-Bayes' approach, although it, really, is of an anti-Naïve-Bayes' paradigm. We provide here the analytical and experimental results for the two-dimensional Uniform, Doubly-exponential and Gaussian and Rayleigh distributions, and also clearly specify the way by which one should extend the results for higher dimensions.

Additional Metadata
Keywords Classification using order statistics (OS), Moments of OS
Persistent URL dx.doi.org/10.1007/978-3-319-00969-8_2
Series Advances in Intelligent Systems and Computing
Citation
Thomas, A. (A.), & Oommen, J. (2013). Classification of multi-dimensional distributions using order statistics Criteria. Advances in Intelligent Systems and Computing. doi:10.1007/978-3-319-00969-8_2