Coupling Recursive Hyperspheric Classification with Linear Discriminant Analysis for Improved Results

Recursive Hyper spheric Classification (RHC) can accurately and succinctly classify large datasets by dissecting labeled vectors into their constituent groups, or hyper spheres. While RHC is a powerful classification tool, coupling RHC with other linear classifiers enhances the ability and accuracy...

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Bibliographic Details
Published in2013 10th International Conference on Information Technology: New Generations pp. 596 - 601
Main Authors Reed, Salyer B., Reed, Tyson R. C., Dascalu, Sergiu M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2013
Subjects
Classification algorithms
Couplings
Dimensional Reduction
Euclidean distance
Linear discriminant analysis
Machine Learning
Recursive Hyperspheric Classification
Support vector machine classification
Vectors
Wine Dataset
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Summary:Recursive Hyper spheric Classification (RHC) can accurately and succinctly classify large datasets by dissecting labeled vectors into their constituent groups, or hyper spheres. While RHC is a powerful classification tool, coupling RHC with other linear classifiers enhances the ability and accuracy of the classification system, improving recognition of unlabeled vectors. In this paper, RHC is paired with Linear Discriminant Analysis (LDA) for improved classification and recognition rates.
DOI:10.1109/ITNG.2013.91