## 5 Results
### Development of XAI in Different Application Domains
#### 5.3.3 Methods for Explainability  
The available methods for adding explainability to the existing and proposed AI/ML models were initially clustered on the basis of three properties: (i) the stage of generating an explanation; (ii) the scope of the explanation; and (iii) the form of the explanation. Figure 11 \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Model Types** & **Models** & **Count** & **References** \\ \hline Ensemble Models (EMs) & Adaptive Boosting (AdaBoost); Explainable Unsupervised Decision Trees (eUD3.5); eXtreme Gradient Boosting (XGBoost); Gradient Boosting Machines (GBM); Isolation Forest (IF); Random Forest (RF); Random Shapelet Forest (RSF) & 21 & [23, 47, 48, 49, 50, 55, 63, 65, 102, 106, 111, 112, 114, 123, 130, 139, 142, 145, 163, 170, 172] \\ \hline Tree-Based Models (TB) & Classification and Regression Tree (CART); Conditional Inference Tree (CTree); Decision Tree (DT); Fast and Frugal Trees (FFTs); Fuzzy Hoeffding Decision Tree (FHDT); J48; One-Class Tree (OCTree); Multi-Operator Temporal Decision Tree (MTDT); Recursive Partitioning and Regression Trees (RPART) & 10 & [12, 52, 54, 88, 105, 108, 127, 128, 136, 172] \\ \hline Fuzzy Models (FMs) & Big Bang–Big Crunch Interval Type-2 Fuzzy Logic System (BB-BC IT2 FLS); Constrained Interval Type-2 Fuzzy System (CIT2 FS); Cumulative Fuzzy Class Membership Criterion (CFCMC); Fuzzy Unordered Rule Induction Algorithm (FURIA); Hierarchical Fuzzy Systems (HFS); Multi-Objective Evolutionary Fuzzy Classifiers (MOEFC); Wang–Mendal Algorithm of Fuzzy Rule Generation (WM Algorithm) & 09 & [52, 61, 64, 95, 104, 134, 157, 178] \\ \hline Support Vector Machines (SVMs) & SVM with Linear and Radial Basis Function (RBF) Kernels & 08 & [12, 23, 47, 48, 49, 63, 87, 128, 139, 170, 176] \\ \hline Unsorted Models (UMs) & Cartesian Genetic Programming (CGP); Computational Argumentation (CA); Logic Scoring of Preferences (LSP); Preference Learning (PL); Probabilistic Soft Logic (PSL); Sequential Rule Mining (SRM); Trials Rank & 07 & [100, 103, 113, 122, 162, 164, 168] \\ \hline Linear Models (LMs) & Linear Discriminant Analysis (LDA); Logistic Regression (LgR); Linear Regression (LnR) & 06 & [12, 99, 124, 128, 172] \\ \hline Nearest Neighbours Models (NNMs) & k-Nearest Neighbours (kNN); Distance-Weighted kNN (WkNN) & 06 & [12, 106, 128, 139, 156, 170] \\ \hline Neuro-Fuzzy Models (NFMs) & Adaptive Network-Based Fuzzy Inference System (ANFIS); Improved Choquet Integral Multilayer Perceptron (iChIMP); LeNet with Fuzzy Classifier; Mamdani Fuzzy Model; Sugeno-Type Fuzzy Inference System; Zero-Order Autonomous Learning Multiple-Model (ALMMo-0*) & 05 & [82, 90, 116, 118, 169] \\ \hline Case-Based Reasoning (CBR) & CBR-kNN; CBR-WkNN; CBR-PRVC (Pattern Recognition, Validation and Contextualisation) Methodology & 04 & [92, 97, 121, 171] \\ \hline Bayesian Models (BM) & Bayesian Network (BN); Bayesian Rule List (BRL); Gaussian Naive Bayes Classifier/Regressor (GNBC/GNBR) & 03 & [126, 139, 165] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._illustrates the number of articles presenting research works concerning each of the properties. The summary of the clustering is represented in Table 6 where model-specific methods are cross-referenced to the model types described in Section 5.3.2. A good number of model-agnostic (MA) methods were also deployed to provide explainability in the selected articles of this review, such as Anchors [96], Explain Like I'm Five (ELI5) [139], Local Interpretable Model-Agnostic Explanations (LIME) [12], and Model Agnostic Supervised Local Explanations (MAPLE) [65]. LIME was modified and proposed as SurvLIME by [PERSON] et al. [179]. Afterwards, the authors incorporated well-known Kolmogorov-Smirnov bounds to SurvLIME and proposed SurvLIME-KS [57]. The authors also utilised feature importance to generate numeric explanations in several research works [172, 67, 128, 144].