| dc.contributor.author | Miliauskaitė, Jolanta | |
| dc.contributor.author | Kalibatienė, Diana | |
| dc.date.accessioned | 2023-09-18T20:30:49Z | |
| dc.date.available | 2023-09-18T20:30:49Z | |
| dc.date.issued | 2020 | |
| dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/150595 | |
| dc.description.abstract | The development of a data-driven fuzzy inference system (FIS) involves the automatic generation of membership functions and fuzzy if-then rules and choosing a particular defuzzification approach. The literature presents different techniques for automatic FIS development and highlights different challenges and issues of its automatic development because of its complexity. However, those complexity issues are not investigated sufficiently in a comprehensive way. Therefore, in this paper, we present a systematic literature review (SLR) of journal and conference papers on the topic of FIS complexity issues. We review 1 340 papers published between 1991 and 2019, systematize and classify them into categories according to the complexity issues. The results show that FIS complexity issues are classified as follows: computational complexity, fuzzy rules complexity, membership functions complexity, input data complexity, complexity of fuzzy rules interpretability, knowledge inferencing complexity and representation complexity, accuracy and interpretability complexity. The results of this study can help researchers and practitioners become familiar with existing FIS complexity issues, the extent of a particular complexity issue and to decide for future development. | eng |
| dc.format | PDF | |
| dc.format.extent | p. 190-204 | |
| dc.format.medium | tekstas / txt | |
| dc.language.iso | eng | |
| dc.relation.ispartofseries | Communications in Computer and Information Science vol. 1243 1865-0929 1865-0937 | |
| dc.relation.isreferencedby | Scopus | |
| dc.relation.isreferencedby | SpringerLink | |
| dc.source.uri | https://link.springer.com/chapter/10.1007/978-3-030-57672-1_15 | |
| dc.source.uri | https://doi.org/10.1007/978-3-030-57672-1_15 | |
| dc.title | Complexity issues in data-driven fuzzy inference systems: Systematic literature review | |
| dc.type | Straipsnis konferencijos darbų leidinyje Scopus DB / Paper in conference publication in Scopus DB | |
| dcterms.references | 114 | |
| dc.type.pubtype | P1b - Straipsnis konferencijos darbų leidinyje Scopus DB / Article in conference proceedings Scopus DB | |
| dc.contributor.institution | Vilniaus universitetas | |
| dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
| dc.contributor.faculty | Fundamentinių mokslų fakultetas / Faculty of Fundamental Sciences | |
| dc.subject.researchfield | T 007 - Informatikos inžinerija / Informatics engineering | |
| dc.subject.vgtuprioritizedfields | IK0303 - Dirbtinio intelekto ir sprendimų priėmimo sistemos / Artificial intelligence and decision support systems | |
| dc.subject.ltspecializations | L106 - Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies | |
| dc.subject.en | membership function | |
| dc.subject.en | FIS | |
| dc.subject.en | issue | |
| dc.subject.en | limitation | |
| dc.subject.en | complexity | |
| dcterms.sourcetitle | Databases and information systems: 14th International Baltic conference, DB&IS 2020, Tallinn, Estonia, June 16–19, 2020: proceedings | |
| dc.publisher.name | Springer | |
| dc.publisher.city | Cham | |
| dc.identifier.doi | 10.1007/978-3-030-57672-1_15 | |
| dc.identifier.elaba | 67109550 | |
