COURSE UNIT TITLE

: INFORMATION AGGREGATION METHODS WITH FUZZY APPROACHES

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
CSC 5050 INFORMATION AGGREGATION METHODS WITH FUZZY APPROACHES ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSISTANT PROFESSOR RESMIYE NASIBOĞLU

Offered to

Computer Science
Ph.D. in Computer Science

Course Objective

To learn methods of aggregation multiple information in fuzzy decision making systems.

Learning Outcomes of the Course Unit

1   To have basic knowledge about fuzzy aggregation methods and properties.
2   To have knowledge about WABL based methods.
3   To have knowledge about OWA based methods.
4   be able to create decision models by using aggregation methods.
5   To be able to solve problems using aggregation methods.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction. What is fuzzy information
2 Fuzzy sets and fuzzy numbers
3 Defuzzification methods of fuzzy values
4 Aggregation approaches in fuzzy decision making
5 Multiple criteria and aggregated values
6 Weighted aggregation based on levels method (WABL)
7 WABL-based distance and comparison methods
8 Applications based on WABL methods
9 Ordered weighted averaging method (OWA)
10 OWA-based general aggregation methods
11 Applications based on OWA methods
12 Project presentations 1
13 Project presentations 2
14 Project presentations 3

Recomended or Required Reading

1. Yager RR. On Ordered Weighted Averaging Aggregation Operators in Multicriteria Decision Making. IEEE Trans. Systems, Man and Cybernetics; 1988. p. 183-190: 18-1.
2. Yager R. Quantifier Guided Aggregation Using OWA Operators. International Journal of Intelligent Systems 2004; 49 73.
3. Nasibov E. Aggregation of fuzzy values in linear programming problems. Automatic Control and Computer Sciences, 37(2):1-11
4. R.A. Aliev, R.R. Aliev, Soft Computing and Its Applications, by World Scientific Publishing Co., 2001.
5. Jang, J., Sun C., Mizutani E. Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, Prentice Hall, 1997.

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation and discussion format. Besides the taught lecture, group presentations are to be prepared by the groups assigned and presented in a discussion session. In some weeks of the course, results of the homework given previously are discussed.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT/PRESENTATION
2 FIN FINAL EXAM
3 FCG FINAL COURSE GRADE ASG * 0.40 +FIN * 0.60
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) ASG * 0.40 +RST * 0.60


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

resmiye.nasiboglu@deu.edu.tr

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 5 70
Preparation for midterm exam 1 10 10
Preparation for final exam 1 20 20
Preparing presentations 3 15 45
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 191

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.155544
LO.255544
LO.355544
LO.455544
LO.555544

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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