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Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
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ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Pattern Classification (2nd Edition)
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Exploring the linearity of models on the basis of ranked data
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Scheduling computational process on the basis of ranked models and CPL criterion functions
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
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Ranked transformations should preserve a priori given ranked relations (order) between some feature vectors. Designing ranked models includes feature selection tasks. Components of feature vectors which are not important for preserving the vectors order should be neglected. This way unimportant dimensions are greatly reduced in the feature space. It is particularly important in the case of “long” feature vectors, when a relatively small number of objects is represented in a high dimensional feature space. In the paper, we describe designing ranked models with the feature selection which is based on the minimisation of convex and piecewise linear (CPL) functions.