Complexity Issues in Data-Driven Fuzzy Inference Systems: Systematic Literature Review

Miliauskaitė, Jolanta; Kalibatiene, Diana

doi:10.1007/978-3-030-57672-1_15

Cited by 3 publications

(2 citation statements)

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“…For dealing with validity threats regarding the search string (i.e. missing keywords leading to the exclusion of relevant papers), we carried out the primary study during preparation (Miliauskaitė and Kalibatiene, 2020a). Moreover, after performing a first iteration of the search, we have applied the backward snowballing technique to develop a new search string from the already included papers.…”

Section: Review Analysismentioning

confidence: 99%

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A Hybrid Systematic Review Approach on Complexity Issues in Data-Driven Fuzzy Inference Systems Development

Kalibatiene¹,

Miliauskaitė²

2021

Informatica

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The data-driven approach is popular to automate learning of fuzzy rules and tuning membership function parameters in fuzzy inference systems (FIS) development. However, researchers highlight different challenges and issues of this FIS development because of its complexity. This paper evaluates the current state of the art of FIS development complexity issues in Computer Science, Software Engineering and Information Systems, specifically: 1) What complexity issues exist in the context of developing FIS? 2) Is it possible to systematize existing solutions of identified complexity issues? We have conducted a hybrid systematic literature review combined with a systematic mapping study that includes keyword map to address these questions. This review has identified the main FIS development complexity issues that practitioners should consider when developing FIS. The paper also proposes a framework of complexity issues and their possible solutions in FIS development.

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Section: Review Analysismentioning

confidence: 99%

“…Further, we analyse the complexity of a fuzzy inference system (FIS) that uses fuzzy set theory to map inputs to outputs and consists of the following main components (Askari, 2017;Mamdani, 1974;Takagi and Sugeno, 1985;Miliauskaitė and Kalibatiene, 2020a) (see Fig. 1).…”

Section: Introductionmentioning

confidence: 99%