Abstract
Retrieving the most suitable items and sorting them downwards from the best face many challenges. The conformance measures are able to efficiently calculate similarities between the desired value and values of considered items’ attribute regardless of different data types. These measures should be suitably aggregated, because the users usually provide different preferences among atomic conformances and therefore various aggregation functions should be considered. In this paper, we examine conjunctive functions (including non t-norms) as well as averaging and hybrid ones. In the hybrid aggregation, uninorms and ordinal sums of conjunctive and disjunctive functions have shown their perspectives in aggregating conformance measures. Diverse tasks require functions of desired behaviour and properly assigned weights or parameters. Thus, the perspectives for merging aggregation functions with the machine learning to the mutual benefits are outlined.
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This paper was partially supported by the project: VEGA No. 1/0373/18 entitled “Big data analytics as a tool for increasing the competitiveness of enterprises and supporting informed decisions” supported by the Ministry of Education, Science, Research and Sport of the Slovak Republic.
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Hudec, M., Vuc̆etić, M. (2019). Aggregation of Fuzzy Conformances. In: Halaš, R., Gagolewski, M., Mesiar, R. (eds) New Trends in Aggregation Theory. AGOP 2019. Advances in Intelligent Systems and Computing, vol 981. Springer, Cham. https://doi.org/10.1007/978-3-030-19494-9_28
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DOI: https://doi.org/10.1007/978-3-030-19494-9_28
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