Ordinal Classification Using Partially Ordered Feature Sets
نویسندگان
چکیده
This paper presents a method to solve an ordinal classification problem where the objects are described with a set of partially ordered features. To construct an ordinal classification model we aggregate partial orders of features by weighting the incidence matrices of partial order graphs. To describe set of possible solutions we use partial order cones corresponding to the incidence matrices and find a solution of the classification problem as the projection of a vector of response variables to a superposition of the cones. We propose a method of optimum parameter estimation for the ordinal classification model. The method is applied to the IUCN Red List categorization problem.
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