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Preference Learning Based Decision Map Algebra: Specification and Implementation

Abubahia, Ahmed ORCID: https://orcid.org/0000-0002-1775-7208, Chakhar, Salem and Cocea, Mihaela (2019) Preference Learning Based Decision Map Algebra: Specification and Implementation. In: Ju, Z., Yang, L., Yang, C., Gegov, A. and Zhou, D., (eds.) Advances in Computational Intelligence Systems. UKCI 2019. Advances in Intelligent Systems and Computing (1043). Springer

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Decision Map Algebra (DMA) is a generic and context independent algebra, especially devoted to spatial multicriteria modelling. The algebra defines a set of operations which formalises spatial multicriteria modelling and analysis. The main concept in DMA is decision map, which is a planar subdivision of the study area represented as a set of non-overlapping polygonal spatial units that are assigned, using a multicriteria classification model, into an ordered set of classes. Different methods can be used in the multicriteria classification step. In this paper, the multicriteria classification step relies on the Dominance-based Rough Set Approach (DRSA), which is a preference learning method that extends the classical rough set theory to multicriteria classification. The paper first introduces a preference learning based approach to decision map construction. Then it proposes a formal specification of DMA. Finally, it briefly presents an object oriented implementation of DMA.

Item Type: Book Section
Status: Published
DOI: https://doi.org/10.1007/978-3-030-29933-0_29
School/Department: School of Science, Technology and Health
URI: https://ray.yorksj.ac.uk/id/eprint/7937

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