Methods of group decision making Field of study: Computer Science
Programme code: W4-S2INA19.2020

Module name: Methods of group decision making
Module code: W4-INA-S2-20-F-MPDG
Programme code: W4-S2INA19.2020
Semester:
  • summer semester 2021/2022
  • winter semester 2021/2022
  • summer semester 2020/2021
Language of instruction: English
Form of verification: course work
ECTS credits: 4
Description:
The course aims at presenting issues related to multiple classifier systems and fusion methods used when making group decisions. The subject will also cover selected topics from game theory. Content: 1. Topology and architecture of multiple classifier systems. 2. Methods of constructing combined classifiers: Bagging, Boosting, methods of selecting variables. 3. Methods for combining base classifiers' prediction results: fusion methods from the abstract, rank and measurement levels. 4. The problem of diversity of base models. 5. Introduction to the two-player games, payoff matrix and the Nash equilibrium. 6. Introduction to the n-player games and the Shapley value.
Prerequisites:
(no information given)
Key reading:
1. Lidmila I. Kunchewa,Combining Pattern Classifiers, Methods and Algorithms, JohnWiley & Sons, Inc., Hoboken, New Jersey 2004 2. Philip D. Straffin, Game Theory and Strategy (New Mathematical Library, No. 36), 1993
Learning outcome of the module Codes of the learning outcomes of the programme to which the learning outcome of the module is related [level of competence: scale 1-5]
The student knows the topology and architecture of multiple classifier systems, building combined classifiers and techniques for the fusion of based models' predictions. [M_001]
 K_W02 [1/5] K_W05 [2/5] K_W09 [1/5]
The student knows the fundamental issues related to two-player and n-player games, i.e. the payoff matrix, the Nash equilibrium and the Shapley value. [M_002]
 K_W01 [1/5]
The student can choose appropriate architecture and topology of multiple classifier systems to the considered problem. They can carry out the process of building combined classifiers and apply the proper fusion method. [M_003]
 K_U03 [1/5] K_U08 [1/5] K_U09 [1/5]
The student can use the selected programme to perform the analysis using multiple classifier systems. [M_004]
 K_U09 [1/5]
Type Description Codes of the learning outcomes of the module to which assessment is related
Examination reports [W_001]
The student should prepare written reports and their oral presentation in the specified time to verify skills obtained during problems' solving.
 M_001 M_002 M_003 M_004
Test [W_002]
The test verifies the knowledge and skills based on the analysis of tasks completed.
 M_001 M_002 M_003
Form of teaching Student's own work Assessment of the learning outcomes
Type Description (including teaching methods) Number of hours Description Number of hours
lecture [Z_001]
The lectures present concepts and facts listed in the module and illustrate them with numerous examples
 15
The students self-study the content of the lectures and the literature
 15 Test [W_002]
laboratory classes [Z_002]
During the laboratory classes, the students perform exercises with the teacher's help, which develops the skills listed in the module's set of learning outcomes.
 30
The students improve the skills listed in the set of learning outcomes of the module.
 60 Examination reports [W_001] Test [W_002]
Attachments
Module description (PDF)
Information concerning module syllabuses might be changed during studies.
Syllabuses (USOSweb)
Semester Module Language of instruction
(no information given)