# A Bagged Ensemble Convolutional Neural Networks Approach to Recognize Insurance Claim Frauds
## 3 Materials and Methods
### Comparison Paradigms
One of the key advantages of GP is its ability to model non-linear and complex relationships in the data, which makes it well-suited for detecting anomalies in high-dimensional data [41]. **Random Forest (RF)**: Random Forests are an ensemble learning method that constructs a multitude of decision trees at training time. For classification tasks, the forest outputs the class that is the mode of the classes of the individual trees. For regression tasks, the Forest outputs the mean prediction of the individual trees. The Random Forest algorithm has demonstrated its proficiency in identifying fraudulent activities in financial and credit card transactions [40]. One of the key benefits of utilizing this algorithm is its capability to process high-dimensional and imbalanced datasets, which are prevalent in fraud detection scenarios [42]. Furthermore, it gives an insight into the most vital features for fraud detection by providing feature importance information. **Multilayer perceptron (MLP)**: A multilayer perceptron is a type of artificial neural network that can be used for supervised learning. It consists of an input layer, hidden layers, and an output layer. The hidden layers are composed of neurons with a nonlinear activation function. Multilayer perceptrons are commonly used for tasks such as classification and regression. MLP has been used in various fraud detection problems, such as credit card fraud detection and financial fraud detection [40]. However, MLP requires a large number of labeled data to train effectively, which may not always be available in fraud detection problems. Additionally, it can be sensitive to noise, and the choice of architecture, activation functions, and the number of hidden layers are also important for obtaining good results. **AdaBoost**: Adaptive Boosting is a machine learning meta-algorithm that can be used to improve the performance of other types of learning algorithms. It does this by combining the outputs of the other learning algorithms (known as 'weak learners') into a weighted sum that represents the final output of the boosted classifier. AdaBoost is adaptive in that it adjusts subsequent weak learners to favor those instances which were misclassified by previous classifiers. Adaboost was proposed by [PERSON] and [PERSON] [43] and won the 2003 Godel Prize for their work. A study by [PERSON] et al. [44] aimed to detect credit card fraud using machine learning algorithms, taking into account the sensitivity of working with real-world credit card information. The research employed a variety of machine learning techniques, including standard neural networks and deep learning models. The study started by testing standard algorithms such as SVM, NB and DL, and then implemented hybrid methods by combining AdaBoost and majority voting techniques. The proposed method achieves good accuracy rates in detecting fraud cases. **Quadratic Discriminant Analysis (QDA)**: Quadratic discriminant analysis is a statistical classification technique, it is a variant of Linear Discriminant Analysis (LDA). QDA makes assumptions about the distribution of data in each class, and is therefore considered a generative model. **GaussianNB**: Naive Bayes (NB) is widely used in financial fraud problems [40]. GaussianNB is based on the assumption that the data are distributed according to a Gaussian distribution, which means that it is clustered around a mean value. The classifier assigns each data point a class label based on the class with the highest probability of occurring.