Fuzzy Sets in Decision Making Processes
- Credits: 6
- Ending: Examination
- Range: 2P + 2C
- Semester: winter
- Year: 2
- Faculty of Economic Informatics
Teachers
Included in study programs
Teaching results
In particular, students acquire the following abilities:
A. understanding the semantic uncertainty of real-world and appropriately handling by fuzzy sets and fuzzy logic,
B. creating flexible database queries,
C. logically aggregating elementary conditions,
D. developing and interpreting linguistic summaries from data,
E. applying fuzzy inference and classification models,
F. handling and managing work with imprecise data in databases,
G. applying acquired knowledge and skills in solving real-world tasks,
H. gaining the overview of the role of fuzzy logic in explainable artificial intelligence.
Indicative content
1. Introduction into fuzzy sets and fuzzy logic, and comparison with the classical logic and set theory.
2. Fuzzy arithmetic.
3. Logic aggregation functions and their applications in evaluating entities and summarizing information from data.
4. Flexible (fuzzy) relational database queries.
5. Empty and overabundant problems in queries.
6. Linguistic summaries on numeric and categorical data.
7. Fuzzy inference (Mamdani and Sugeno model, defuzzification).
8. Flexible rule-based systems and IF-THEN rules (developing rule-based systems and evaluating their quality).
9. Fuzzy relational databases (basic model and fuzzy meta model).
10. Querying on fuzzy relational databases and data warehouses.
11. Possibility and necessity measures in data evaluation.
12. Overview of the advanced concepts: type II fuzzy sets, hesitant fuzzy sets, intuitionistic fuzzy sets
13. Fuzzy logic in explainable artificial intelligence.
Support literature
HUDEC M. (2015). Fuzzy logika pre hospodársku informatiku. Ekonóm, Bratislava.
KOLESÁROVÁ A., KOVÁČOVÁ M. (2004). Fuzzy množiny a ich aplikácie. Slovenská technická univerzita v Bratislave, Bratislava.
KLIR, G., YUAN, B. (1995). Fuzzy sets and fuzzy logic, theory and applications. Prentice Hall, New Jersey.
SILER W., BUCKLEY, J. (2005). Fuzzy expert systems and fuzzy reasoning. John Wiley & Sons, Inc, New Jersey.
ZIMMERMANN H. J. (2001). Fuzzy set theory – and its applications. Kluwer Academic Publishers, London.
HUDEC M. (2016). Fuzziness in Information Systems - How to Deal with Crisp and Fuzzy Data in Selection, Classification, and Summarization. Springer, Cham.
GALINDO, J. (Ed.) (2008). Handbook of Research on Fuzzy Information Processing in Databases. IGI Global,Hershley.
Requirements to complete the course
Exam 60% The exam consists of two parts: the evaluation of the theoretical knowledge and knowledge of modelling specific tasks. The first part, verifies the achievement level of the teaching results A., D., F., H., whereas the second part verifies the level of the teaching results B., C., E., G.
Assignments during the semester 40% The purpose of seminars is to develop and defend the tasks related to modeling uncertainties and a test. The evaluation of the students also their activity during the semester. The following teaching results are evaluated B., C., D., E. G.
Student workload
Total study load (in hours):
6 credits x 26 teaching hours = 156 h
Distribution of study load:
lectures and seminars participation: 52 h
seminar participation: 24 h
tasks and test preparation: 40 h
preparation of exam: 40 h
Language whose command is required to complete the course
slovak
Date of approval: 11.03.2024
Date of the latest change: 18.05.2022