Ranked Regression CPL Criterion Function Prognostic Models Decomposition of Ranked Models Chain Split and Computations in Practical Rule Mining Computing Classification Systems
Institute of Information Theories and Applications FOI ITHEA
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