Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
A prototype application of fuzzy logic and expert systems in education assessment
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Parameter conditions for monotonic Takagi-Sugeno-Kang fuzzy system
Fuzzy Sets and Systems - Fuzzy systems
On the use of fuzzy inference techniques in assessment models: part I--theoretical properties
Fuzzy Optimization and Decision Making
On the monotonicity of fuzzy-inference methods related to T-S inference method
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Ensemble of constraint handling techniques
IEEE Transactions on Evolutionary Computation
A fuzzy inference system-based criterion-referenced assessment model
Expert Systems with Applications: An International Journal
IEEE Transactions on Fuzzy Systems
Fuzzy set approach to the assessment of student-centered learning
IEEE Transactions on Education
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Hybrid approaches for approximate reasoning
Special issue: Computational intelligence models for image processing and information reasoning
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Computational intelligence models for image processing and information reasoning
| Hi-index | 0.00 |
The use of a Fuzzy Inference System FIS as a part of Criterion-referenced Assessment CRA is not new. Nevertheless, there are several limitations in combining FIS models and CRA, as follows. i It is difficult to maintain the monotonicity property of the FIS-based CRA model; ii it is difficult and impractical to form a complete fuzzy rule base when the number of required rules is large, and iii reducing fuzzy rules may cause the “tomato classification” problem. In this paper, a practical solution to overcome these limitations is provided. We adopt the sufficient conditions i.e., a mathematical foundation as a set of governing equations for designing fuzzy membership functions and fuzzy rules to preserve the monotonicity property. In this paper, our works in [21] is extended and a new procedure that comprises of the sufficient conditions, fuzzy rule reduction and a monotonicity-preserving similarity reasoning SR is proposed. The new framework reduces the number of fuzzy rules gathered from an assessor i.e., selected rules with a proposed fuzzy rule selection approach. Selected rules are identified in such that the unselected rules can be deduced via a monotonicity-preserving SR technique. We formulate the process of minimizing the number of selected rules as a constrained optimization problem and a genetic algorithm GA technique is implemented. Unselected rules are predicted with a proposed monotonicity-preserving SR scheme. The proposed approach contributes to a solution to reduce the number of fuzzy rules to be gathered from an assessor while maintaining the monotonicity property. Besides, this paper also contributes to a new application of SR in education assessment. The proposed approach is evaluated with a case study relating to a lab assessment in Universiti Malaysia Sarawak UNIMAS.