## 2 Related Work  
One of the earliest ensemble techniques used to calculate feature importance is Random Forest (RF), proposed by [PERSON] [26]. RF is a ML model that forms from an ensemble of decision trees via random subspace methods [27]. Besides prediction, RF computes the overall feature importance by averaging those determined by each decision tree in the ensemble. RF feature importance is quantified depending on how many times a feature branches out in the decision tree, based on the Gini impurity metric. Alternatively, decision trees also calculate feature importance as Mean Decrease Accuracy (MDA) or more commonly known as PI, by permutating the subset of features in each decision tree and by calculating how much accuracy decreases as a consequence. Using the knowledge of ensemble feature importance from weak learners, [PERSON] et al. [28] proposes an ensemble learning based on generalised additive models (GAM) to estimate feature importance and confidence interval of prediction output. Similar to Bagging, the average of each weaker additive model generates the ensemble predictions. The feature importance scores are generated using the following steps: (i) generate output and calculate performance for individual predictions based on a specific performance criterion; (ii) permute each feature and recalculate error for Out-Of-Bag (OOB) predictions; (iii) calculate the partial importance score based on OOB predictions; and (iv) repeat steps (i) to (iii) for each additive model and different forms of evaluation. The authors argue that the importance of each feature should be optimised according to the performance criteria most relevant to the feature to obtain the most accurate feature importance score. The importance of the GAM ensemble-based feature is subsequently applied to identify essential features in churn prediction to determine customers likely to stop paying for particular goods and services. To determine the ten most relevant features, the authors use Receiver Operating Characteristic and top-decile lift. The authors observed that the sets of important features overlapped, but their rank order was different when using ROC and lift. The different rank orders show that feature importance is affected by the evaluation criteria. Both [PERSON] and [PERSON] et al. uses only a single ML model with one type of feature importance method to calculate the ensemble feature importance, which restricts the potential to improve accuracy and to reduce variance. To overcome this limitation, [PERSON] and [PERSON] [29] employed a stacked ensemble model to forecast the daily average of air particle concentrations in China. The stacked ensemble consists of four different ML models, namely, Least Absolute Shrinkage and Selection Operator (LASSO) [30], adaptive boosting (AdaBoost) [31], XGBoost, and Multi-layer Perceptron with Support Vector Regression (SVR) as the meta-regressor. The authors used a combination of feature selection and model generated method to determine feature importance, determined from Stability Feature Selections, the XGBoost model, and the AdaBoost model. Their outputs were subsequently averaged for the final ranking of features. AdaBoost and GBT use the Mean Decrease Impurity (MDI) based on the Gini importance; the sample frequency spectrum is based on maximum feature scores using Bayesian Information Criterion [32]. The top ten features are selected for evaluation. While [PERSON] and [PERSON] used multiple ML models and one feature importance approach, to the best of our knowledge, there has not been further investigations to improve feature importance quantification using multiple models and multiple feature importance methods. Finally, as we can see in this section, there is minimal in-depth systematic investigation of how ensemble feature importance fusion works. Therefore, it is imperative that we investigate interpretability methods and ensemble feature importance fusion under different data conditions.