Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/1199

 Title: Structuring of Ranked Models Authors: Bobrowski, Leon Keywords: Ranked RegressionCPL Criterion FunctionPrognostic ModelsDecomposition of Ranked ModelsChain Split and Computations in Practical Rule MiningComputing Classification Systems Issue Date: 2009 Publisher: Institute of Information Theories and Applications FOI ITHEA Abstract: Prognostic procedures can be based on ranked linear models. Ranked regression type models are designed on the basis of feature vectors combined with set of relations defined on selected pairs of these vectors. Feature vectors are composed of numerical results of measurements on particular objects or events. Ranked relations defined on selected pairs of feature vectors represent additional knowledge and can reflect experts' opinion about considered objects. Ranked models have the form of linear transformations of feature vectors on a line which preserve a given set of relations in the best manner possible. Ranked models can be designed through the minimization of a special type of convex and piecewise linear (CPL) criterion functions. Some sets of ranked relations cannot be well represented by one ranked model. Decomposition of global model into a family of local ranked models could improve representation. A procedures of ranked models decomposition is described in this paper. URI: http://hdl.handle.net/10525/1199 ISSN: 1313-0455 Appears in Collections: Book 08 Classification Forecasting Data Mining

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