A Review on the Interpretability-Accuracy Trade-Off in Evolutionary Multi-Objective Fuzzy Systems (EMOFS)
Abstract
:1. Introduction
2. Evolutionary Multi-Objective Optimization
3. Handling Interpretability-Accuracy Trade-Off using MOEAs in Fuzzy Systems
Approaches developed | Focus | References |
---|---|---|
Maximization of the number of correctly classified patterns along with minimization of the number of rules and fuzzy rule selection represented as a combinatorial optimization problem | Accuracy improvement & complexity minimization | [36] |
Association of rule weights in rules also called certainty factor | Accuracy improvement | [37,38] |
Multiple consequents in a rule | Accuracy improvement | [39] |
Use of fine fuzzy partition (over-fitting), multiple fuzzy grid approach | Accuracy Improvement | [40] |
Applying independent membership functions | Accuracy & Scalability improvement | [41] |
Use of multi-dimensional fuzzy membership function | Accuracy and scalability improvement | [42,43] |
Use of tree-type fuzzy partitions | Accuracy & Scalability Improvement | [44,45] |
Scalability and hierarchical fuzzy systems | Accuracy & Scalability Improvement | [46] |
Use of don’t care conditions/ scalability improvement/input selection for each rule (rule wise input selection) | Complexity minimization | [47] |
3.1. MOEAs with Two Objectives
3.2. MOEA with Three Objectives
3.3. Improving the Search Ability of the MOEAs
3.4. MOEA to Design Ensemble Classifiers
3.5. MOEA for Scaling Functions and Fine Fuzzy Partition
3.6. Approaches Related to User Preferences
3.7. Approaches Related to High Dimensional Problems
3.8. Semantic Co-intension Approach
3.9. Context Adaptation
3.10. EMO Approaches for Data Mining Applications
3.11. Other Specific Applications Developed Using EMO
4. Burning Research Issues
- Improvement in the interpretability of a system by selecting parameters like number of inputs, number of rules, rule length, fuzzy partition granularity, membership function separability, linguistic modifiers, linguistic hedges etc. Choosing these parameters may be considered in order to develop new interpretability indexes.
- Handling Interpretability-Accuracy (I-A) Trade–Off using EMO [5,72,124] is a critical issue because interpretability and accuracy are the features conflicting with each other. One can be improved at the cost of the other, which leads to generation of multiple sets of solutions instead of any single solution.
- An increment in the number of objectives degrades the performance of any EMO algorithm. Hence, improvement of the performance of MOEA when the numbers of objectives are high is a big research line. It helps to deal with the High Dimensional Problems [125], leading to the development of Hierarchical Fuzzy Systems.
- Handling large and multi-dimensional data sets [127] by EMO algorithms.
- Generation of mechanisms for interpretable explanations for fuzzy reasoning and inference mechanism, quantification of explanation ability of FRBS [128].
5. Conclusion and Future Scope
S. No. | EMO Used | References |
---|---|---|
1 | SPEA2 | [55,57,58,60,62,65,85,88,109,117,118] |
2 | NSGA-II | [55,56,57,60,61,65,68,72,76,81,85,89,92,93,96,101,105,106,107,110,111,112,115,117] |
3 | SPEA2ACC | [64,65,66] |
4 | (2 + 2) PAES | [56,61,63] |
5 | (2 + 2) M-PAES | [59,69,74,75,82,83,84,108] |
6 | HILK EMO | [86,110] |
7 | Fuzzy GBML | [94,95,97,102] |
8 | PMOCCA | [90] |
S. No. | Type of the problem identified | References |
---|---|---|
1 | Classification of Problems | [52,53,54,67,68,77,78,79,90,91,92,93,94,95,96,97,99,100,101,102,104,105,106,107,110,112,113,114,115,116,117] |
2 | Regression | [69,85,87,88,109] |
3 | Linguistic FRBS | [55,56,57,58,59,60,61,62,63,64,65,74,75,76,80,82,83,86,89,108,111,118] |
4 | Function Approximation Problems | [71,72] |
5 | TS Type FRBS | [119] |
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Shukla, P.K.; Tripathi, S.P. A Review on the Interpretability-Accuracy Trade-Off in Evolutionary Multi-Objective Fuzzy Systems (EMOFS). Information 2012, 3, 256-277. https://doi.org/10.3390/info3030256
Shukla PK, Tripathi SP. A Review on the Interpretability-Accuracy Trade-Off in Evolutionary Multi-Objective Fuzzy Systems (EMOFS). Information. 2012; 3(3):256-277. https://doi.org/10.3390/info3030256
Chicago/Turabian StyleShukla, Praveen Kumar, and Surya Prakash Tripathi. 2012. "A Review on the Interpretability-Accuracy Trade-Off in Evolutionary Multi-Objective Fuzzy Systems (EMOFS)" Information 3, no. 3: 256-277. https://doi.org/10.3390/info3030256