The Efficiency-Accuracy-Complexity Score (EACS) is a comprehensive metric that considers accuracy, training efficiency, and model complexity in evaluating the performance of anomaly detection models. To further refine the EACS for better evaluation of model performance, several enhancements can be considered:
Weight Adjustment: Fine-tuning the weights assigned to accuracy, training time efficiency, and model complexity components in the EACS formula can provide a more nuanced assessment. By adjusting these weights based on specific use cases or priorities, the EACS can better reflect the relative importance of each aspect in different scenarios.
Incorporating Additional Metrics: While F1 score, training time efficiency, and parameter count are essential components of EACS, incorporating additional metrics such as interpretability, scalability, or robustness could offer a more holistic view of model performance. These supplementary metrics can capture aspects that may not be fully represented by existing components.
Normalization Techniques: Implementing normalization techniques to standardize values across different datasets or models can enhance comparability and ensure fair evaluations. Normalizing input data ranges or scaling individual component scores before calculating EACS could lead to more consistent assessments.
Dynamic Thresholds: Introducing dynamic thresholds within the EACS calculation based on dataset characteristics or optimization goals could improve adaptability and responsiveness to varying requirements. Dynamic thresholds that adjust based on specific criteria like dataset complexity or anomaly prevalence rates can tailor evaluations to specific contexts.
Validation Studies: Conducting validation studies with diverse datasets and benchmark models to validate the effectiveness of EACS in capturing key aspects of model performance would provide empirical evidence supporting its refinement process.
By implementing these refinements and considering additional factors relevant to anomaly detection tasks, the EACS can evolve into a more robust and versatile metric for evaluating model performance comprehensively.