Data Hierarchies for Generalization of Imprecise Data

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Author(s): Frederick Petry (Naval Research Laboratory, USA)and Ronald R. Yager (Iona College, USA)
Copyright: 2023
Pages: 14
Source title: Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch117

Keywords: Data Analysis and Statistics / Information Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

Issues related to managing imprecise data in areas as diverse as spatial and environmental data, forensic evidence, and economics must be dealt with for effective decision making. To make use of such information, the authors settle on how the various pieces of data can be used to make a decision or take an action. This involves some sort of summarization and generalization of the pieces of data as to what conclusions they can support. To address these issues, use of fuzzy, interval valued, and intuitionistic concept hierarchies for generalization can extend previous approaches to deal with the uncertainty of data. A number of approaches to characterizing such decompositions for the resolution of the evidence using these hierarchies is also needed. The characterization of hierarchies indicates that set decompositions are needed to represent the uncertainty of the hierarchies. To characterize these decompositions, granularity measures and overlap measures must be developed and examples of each discussed. Additionally, information measures can be introduced to be used for these evaluations.

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Data Hierarchies for Generalization of Imprecise Data

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