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Applying fuzzy measures and nonlinear integrals in data mining. (English) Zbl 1086.68613
Summary: The paper gives an overview of applying fuzzy measures and relevant nonlinear integrals in data mining, discussed in five application areas: set function identification, nonlinear multiregression, nonlinear classification, networks, and fuzzy data analysis. In these areas, fuzzy measures allow us to describe interactions among feature attributes towards a certain target (objective attribute), while nonlinear integrals serve as aggregation tools to combine information from feature attributes. Values of fuzzy measures in these applications are unknown and are optimally determined via a soft computing technique based on given data.

MSC:
68T37 Reasoning under uncertainty in the context of artificial intelligence
28E10 Fuzzy measure theory
68T05 Learning and adaptive systems in artificial intelligence
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