## 3 Approaches
### Ensemble  
Ensemble methods are known as machine learning techniques that consist of a set of classification models as shown in Figure 3. The point of aggregating them together is to take into account their weighted predictions as a vote. This type of machine learning method is performed in order to enhance the classification accuracy and depend more on several models rather than one model (best model). [PERSON] et al. [150] presented an ensemble approach that combines different classifiers, which are, Random Forest (RF), Multilayer Perceptron (MLP), and K-Nearest Neighbour (k-NN) for detectiensemble model was evaluated on the Yelp dataset alongside using different types of feature selection methods to select the best subset of features. The proposed approach outperforms the individual classifiers (RF, MLP, and k-NN) and state-of-the-art methods.  
Moreover, [PERSON]. [151] proposed a novel study for spam reviews detection using ensemble machine learning methods. In order to improve the accuracy of classification, an additional study was performed to analyze features in combination with ensemble methods. Further, four ensembles are used, including, Extra Tree, Bagging, Random Forest, and Boosting. The ensemble method obtained the best results compared with other methods. Another recent work applied the ensemble model for the identification of spam reviews [152]. Four different steps are used in this study, scilicet, Data resampling, Feature pruning, Parameters optimization, and classifier ensembling. Then the resampling technique is employed to solve the imbalance problem. The results verified the performance dominance of the model against other methods in spam reviews detection.  
Additionally, the authors of [153] introduced a spam reviews detection method based on the ensemble machine learning technique. Three various models are given and trained on multi-view learning techniques to ensemble them together for majority prediction. Then they extract the text information of the reviews through parallel convolution neural networks (CNN) and bag-of-n-grams. The CNN architecture employed the n-gram embeddings as input and extract feature representations of reviews by using parallel convolutional blocks. The evaluation phase takes place on Yelp Filtered dataset with a 92% rate in terms of F1 scores. While the study of [154] built a detection framework for spam reviews using single and ensemble models. Also, the work proposed two random sampling methods for solving the class imbalance issue. The experimental results show that the sampling methods (random under and over-sampling) enhanced the classification accuracy of spam reviews. Besides, the Adaptive Boosting ensemble obtained the highest results for smaller datasets, while single classifiers achieved best when the dataset was larger.  
The indication of the previous works suggested that using more than one classification model achieved better results in detecting spam reviews. In other words, taking the vote weighting of several classification models is better than relying on the best model, where the performance is more reliable due to the fact that various decisions are taken.