## 6 Conclusions and Future Work  
In this paper, we presented a novel framework to perform feature importance fusion using an ensemble of different ML models and different feature importance methods called MME. Overall, our framework performed better than traditional single model-agnostic approaches, where only one feature importance method with multiple ML models is employed. Additionally, we compared different ensemble strategies for our MME Framework such as measurements of central tendency such as median, mean, and mode and anomaly detection methods such as box-whiskers, Modified Thompson Tau test, and majority vote. In addition, we introduced a new ensemble strategy named RATE that combined rank correlation and majority vote. Furthermore, we studied the efficacy of MME Framework and SME on a combination of three different factors: (i) noise level, (ii) percentage of informative features, and (iii) the number of features. We found that different noise levels had a minimal effect on feature importance, whereas the feature importance error increased with the percentage of informative features and number of features in data. For the case studies investigated, the MME Framework with majority vote is often the best performing method for all three data factors, followed by the MME Framework with RATE and mean. When the number of features and the number of informative features are low, the performance of MME Framework is only marginally better than SME. We showed that the MME Framework avoids the worst-case scenario in predicting feature importance. Additionally, the MME Framework is able to increase the quality of feature importance estimates by making it more robust against noise, biases, and anomalies. Despite its advantages, there are several shortcomings in the proposed method and our experimental approach that can be improved in future work. One improvement would be to include more feature importance methods and ML models into the MME Framework to increase the number of methods to investigate their impact on variance and in overall consensus of importance. Another possibility is to integrate the feature importance ensemble into a neural network to internally induce bias in the neural networks to focus on relevant features to decrease training time and to improve accuracy. Finally, for the experimental aspect, several other factors could potentially affect the feature importance estimates, such as hyperparameter optimisations of ML models, imbalance of the datasets, the presence of anomalies in data, and the efficacy of the framework in complex real-world data should be investigated. The following are available at [[https://www.mdpi.com/article/10.3390/app112411854/s1](https://www.mdpi.com/article/10.3390/app112411854/s1)]([https://www.mdpi.com/article/10.3390/app112411854/s1](https://www.mdpi.com/article/10.3390/app112411854/s1)), Figure S1: Average feature importance error between SME and MME with train and test dataset. (RMSE). Figure S2: Average feature importance error between SME and MME with train and test dataset. (\(R^{2}\)). Figure S3: Effect of all noise level on all feature importance methods (RMSE). Figure S4: Effect of all noise level on all feature importance methods (\(R^{2}\)). Figure S6: Effect of noise levels on ensemble feature importance. (\(R^{2}\)). Figure S7: Effect of feature informative level on all ensemble feature importance methods. (RMSE). Figure S8: Effect of feature informative levels on ensemble feature importance methods. (RMSE). Figure S9: Effect of feature informative level on all ensemble feature importance methods. (\(R^{2}\)). Figure S10: Effect of feature informative levels on ensemble feature importance methods. (\(R^{2}\)). Figure S11: Effect of number of features on all ensemble feature importance methods. (RMSE). Figure S12: Effect of number of features on ensemble feature importance methods. (\(R^{2}\)). Figure S13: Effect of number of features on all ensemble feature importance methods. (\(R^{2}\)).