Abstract/Description
In this paper, we present and compare two-stage type-2 fuzzy logic advisor (FLA) for subjective decision making in the domain of students' performance evaluation. We test our proposed model for evaluating students' performance in our computer science and engineering department at HBCC/KFUPM in two domains namely cooperating training and capstone/senior project assessment where we find these FLAs very useful and promising. In our proposed model, the assessment criteria for different components of cooperative training and senior project are transformed into linguistic labels and evaluation information is extracted into the form of IF-THEN rules from the experts. These rules are modeled using FLS, which then is used as a fuzzy logic advisor (FLA) to make decisions about students' grades. The evaluator's input for the system can be either singleton or non-singleton. Both type-1 and type-2 fuzzy logic based models are implemented and compared with individual expert's evaluation.
Keywords
Decision making, Fuzzy logic, Computer science, Uncertainty, Fuzzy sets, Testing, Computer industry, Engineering profession, Data mining, Education
Session Theme
Artificial Intelligence – I
Session Type
Other
Session Chair
Dr. Sajjad Haider
Start Date
15-8-2009 2:45 PM
End Date
15-8-2009 3:05 PM
Recommended Citation
Malik, O. (2009). Artificial Intelligence – I: Subjective decision making using type-2 fuzzy logic advisor. International Conference on Information and Communication Technologies. Retrieved from https://ir.iba.edu.pk/icict/2009/2009/19
Included in
Computer Sciences Commons, Education Commons, Engineering Commons, Technology and Innovation Commons
Artificial Intelligence – I: Subjective decision making using type-2 fuzzy logic advisor
In this paper, we present and compare two-stage type-2 fuzzy logic advisor (FLA) for subjective decision making in the domain of students' performance evaluation. We test our proposed model for evaluating students' performance in our computer science and engineering department at HBCC/KFUPM in two domains namely cooperating training and capstone/senior project assessment where we find these FLAs very useful and promising. In our proposed model, the assessment criteria for different components of cooperative training and senior project are transformed into linguistic labels and evaluation information is extracted into the form of IF-THEN rules from the experts. These rules are modeled using FLS, which then is used as a fuzzy logic advisor (FLA) to make decisions about students' grades. The evaluator's input for the system can be either singleton or non-singleton. Both type-1 and type-2 fuzzy logic based models are implemented and compared with individual expert's evaluation.
