- Prerequisites:
- Python 3.9
- Java (OpenJDK) 17
- Configure project (install all Python dependencies)
- Download compliant ML-LDM binary release (currently supported
v0.7
) from releases page and put it into./benchmark/methods/ml_ldm/scripts/bin/lingvo-dss-all.jar
- Run benchmarking experiments, for example:
python benchmark/comparison/experiment_1.py
- All experiment results are visualized and placed in an artifacts directory, for example in
artifacts/generated_tasks/experiment_1/report/visualization
Parameter | Value |
---|---|
Number of experts | 1 |
Weights of experts | Equal |
Number of alternatives | (3, 5, 7, 9) |
Number of criteria | (5, 10, 15, 20) |
Types of assessments | Numeric |
Parameter | Value |
---|---|
Number of experts | 10 |
Weights of experts | Equal |
Number of alternatives | (3, 5, 7, 9) |
Number of criteria | (5, 10, 15, 20) |
Types of assessments | Numeric, Crisp Linguistic |
Parameter | Value |
---|---|
Number of experts | 10 |
Weights of experts | Automatically assigned |
Number of alternatives | (3, 5, 7, 9) |
Number of criteria | (5, 10, 15, 20) |
Types of assessments | Numeric, Crisp Linguistic |