Abstract
Issues concerning intelligent data analysis occurring in machine learning are investigated. A scheme for synthesizing correct supervised classification procedures is proposed. These procedures are focused on specifying partial order relations on sets of feature values; they are based on a generalization of the classical concepts of logical classification. It is shown that learning a correct logical classifier requires solution of an intractable discrete problem to be solved. This is the dualization problem over products of partially ordered sets. The matrix formulation of this problem is given. The effectiveness of the proposed approach for solution of the supervised classification problem is illustrated on model and real-life data.
This study was partially supported by the Russian Foundation for Basic Research, project no. 19-01-00430-a.
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Djukova, E.V., Masliakov, G.O., Prokofyev, P.A. (2019). Logical Classification of Partially Ordered Data. In: Kuznetsov, S., Panov, A. (eds) Artificial Intelligence. RCAI 2019. Communications in Computer and Information Science, vol 1093. Springer, Cham. https://doi.org/10.1007/978-3-030-30763-9_10
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