Fuzzy Logic in Data Science
- Credits: 8
- Ending: Examination
- Range: 16sP
- Semester: winter
- Faculty of Economic Informatics
Teachers
Included in study programs
Teaching results
Semantic uncertainty or fuzziness is a key feature of many real-world tasks. Completion of this course presupposes the development of key competencies and skills for effective handling this type of uncertainty and solving diverse data science and decision-support tasks.
Knowledge and understanding Students will be able to understand the advanced concepts of computing with words by fuzzy sets and fuzzy logic and aggregation functions. This knowledge is a keystone for task ranging from collecting uncertain data to interpreting mined information linguistically.
Competence Based on the above knowledge, students will be able to model tasks such as: data querying by linguistic terms, modelling flexible dependencies, interpret mined information by short-quantified sentences, classify entities, evaluate entities by logic scores, recommend the most suitable entities and so on.
Skills In this course students will acquire skills related top handling fuzziness related to the data collection, processing and interpreting mined information from diverse data types. These skills are relevant for informing decision makers about the developments in a concise and understandable way.
Indicative content
1. Theory of fuzzy sets and fuzzy logic
2. Type II fuzzy sets and intuitionistic fuzzy sets
3. Modelling hesitance in expert’s knowledge
4. Distances and similarities between concepts without clear boundaries
5. Symmetric and asymmetric logic aggregation functions in evaluating entities
6. Fuzzy measures and capacities in decision making
7. Flexible recommender systems
8. Fuzzy rule-based systems and their explainability and interpretability
9. Theory and features of fuzzy cognitive maps
10. Soft computing evaluation logic in decision support
11. Pattern recognition and classification by fuzzy logic
12. Flexible querying and answering systems
13. Linguistically summarizing developments in data (classic, temporal and time series)
Support literature
1. Alonso J. M., Castiello, C., Magdalena, L., Mencar, C.: Explainable Fuzzy Systems: Paving the way from Interpretable Fuzzy Systems to Explainable AI Systems. Springer. Cham, 2021.
2. Bojadziev, G., Bojadziev, M.: Fuzzy logic for business, finance and management. World Scientific Publishing, London, 2007.
3. Bouchon-Meunier B. Strengths of Fuzzy Techniques in Data Science. In: Kosheleva O., Shary S., Xiang G., Zapatrin R. (eds). Studies in Computational Intelligence, vol 835. Springer, Cham, 2020.
4. Dujmović, J. Soft Computing Evaluation Logic: The LSP Decision Method and Its Applications, IEEE Press and Wiley, 2018.
5. Grabisch, M., Marichal, J.-L., Mesiar, R., Pap, E.: Aggregation Functions. Encyclopedia of Mathematics and its Applications, Cambridge University Press, Cambridge, 2009.
6. Hudec, M.: Fuzziness in Information Systems - How to Deal with Crisp and Fuzzy Data in Selection, Classification, and Summarization. Springer, Cham, 2016.
7. Wang, X, Ruan, D, Kerre, E.E.: Mathematics of Fuzziness. Springer, Berlin Heidelberg, 2009.
8. Xu, Z.: Hesitant fuzzy set theory. Springer, Cham, 2014.
9. Xu, Z.: Linguistic decision making – Theory and Methods. Springer, Berlin Heidelberg, 2012.
Requirements to complete the course
20% seminar work,
20% short essay, resp. project,
60% written exam.
Student workload
208 hours
Participation in lectures - 16 hours
Individual consultations - 42 hours
Project preparation and implementation - 100 hours
Preparation for the final exam - 50 hours.
Language whose command is required to complete the course
Slovak, English
Date of approval: 10.02.2023
Date of the latest change: 17.05.2022