Characterizations and uncertainty measurement of a fuzzy information system and related results

Yu, Guangji

doi:10.1007/s00500-020-05138-6

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…Many other applications also can be found concerning rule extraction [19,21], feature selection and classification [22][23][24][25]. Mathematical properties of fuzzy information systems are under consideration as well [17,26]. The concept of rough sets was extended by analyzing the properties of lower and upper approximations of fuzzy sets [27][28][29][30][31] with corresponding practicable applications.…”

Section: Introductionmentioning

confidence: 99%

Classification with Fuzzification Optimization Combining Fuzzy Information Systems and Type-2 Fuzzy Inference

Tabakov

Chłopowiec

et al. 2021

Applied Sciences

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In this research, we introduce a classification procedure based on rule induction and fuzzy reasoning. The classifier generalizes attribute information to handle uncertainty, which often occurs in real data. To induce fuzzy rules, we define the corresponding fuzzy information system. A transformation of the derived rules into interval type-2 fuzzy rules is provided as well. The fuzzification applied is optimized with respect to the footprint of uncertainty of the corresponding type-2 fuzzy sets. The classification process is related to a Mamdani type fuzzy inference. The method proposed was evaluated by the F-score measure on benchmark data.

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Section: Introductionmentioning

confidence: 99%

Classification with Fuzzification Optimization Combining Fuzzy Information Systems and Type-2 Fuzzy Inference

Tabakov

Chłopowiec

et al. 2021

Applied Sciences

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Uncertainty Measurement for a Hybrid Information System With Images: An Application in Attribute Reduction

Wang

2020

IEEE Access

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A hybrid information system with images (HISI) is an information system (IS) where there exist many kinds of data or attributes (e.g., boolean, categorical, real-valued, set-valued, interval-valued, imaged, decision and missing data or attributes). Uncertainty measurement (UM) is an effective tool for evaluation. This paper inquires into UM for a HISI with application to attribute reduction. The distance between attribute values in a HISI is first constructed. Then, the tolerance relations on an object set of a HISI are obtained by means of this distance. Next, information structures in a HISI are proposed. After that, UM for a HISI is considered by using its information structures. Moreover, effectiveness analysis for the proposed measures is conducted from the angle of statistics. Finally, an application of the proposed measures in attribute reduction for a HISI is given, and the corresponding algorithms are put forward.

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A Systematic Mapping with Bibliometric Analysis on Information Systems Using Ontology and Fuzzy Logic

Kalibatiene

Miliauskaitė

2021

Applied Sciences

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The ontology-based information systems (IS) development is beneficial for analyzing, conceptual modeling, designing, and re-engineering complex IS to be semantically enriched and suitable for sophisticated reasoning on the IS content. On the other hand, fuzzy theory employment to handle uncertainty and fuzziness in IS becomes a hot topic in different practical domains, such as engineering, IS, computer sciences, etc. As such, ontology- and fuzzy-based IS are being developed. Consequently, there is a need to provide a comprehensive systematic mapping study (SMS) to build a structure on the ontology- and fuzzy-based IS field of interest and to grasp the main ideas. This paper presents findings of SMS, based on the papers extracted from Web of Science and Scopus and employing a bibliometric analysis tool to automate keyword mapping. We conclude this paper by summarizing the previous work and identifying possible research trends, which future investigations can extend. The main finding indicates that ontology and fuzzy logic contribute to ISs by expanding traditional IS to be intelligent IS, which is applicable for solving complex, fuzzy, and semantically rich (ontological) information collection, saving, processing, sharing, and reasoning in different application domains according to users’ needs in various countries.

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Characterizations and uncertainty measurement of a fuzzy information system and related results

Cited by 3 publications

References 22 publications

Classification with Fuzzification Optimization Combining Fuzzy Information Systems and Type-2 Fuzzy Inference

Classification with Fuzzification Optimization Combining Fuzzy Information Systems and Type-2 Fuzzy Inference

Uncertainty Measurement for a Hybrid Information System With Images: An Application in Attribute Reduction

A Systematic Mapping with Bibliometric Analysis on Information Systems Using Ontology and Fuzzy Logic

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