## 2 Materials and Methods
### Deep Learning, Kruskal Algorithm and Classifiers
Machine learning classifiers are algorithms and models used to classify samples within a dataset. They use and learn from the features in the dataset to process it so they can classify future samples into the right classes. The classifiers used in the study are explained in order. Naive Bayes: Naive Bayes classifier is a classification algorithm widely used in machine learning and statistics. This algorithm is based on [PERSON] theorem and uses learning data to classify. The naive Bayes classifier calculates the relationship of the properties of the observations (inputs) with the classes according to a certain probability distribution. This  
Figure 5: Feature extraction and classification process in CNN architectures. probability distribution is used to calculate the probability that the given inputs belong to each class, and as a result, the class with the highest probability is selected [21]. Decision trees: Decision trees are a graphical representation that helps classify a dataset based on a set of criteria. These criteria are based on the characteristics of the data and classification is made by creating a decision point for each feature level. The decision trees algorithm is used to classify datasets and all results are displayed as a tree structure. First, a tree structure is created according to the characteristics of the data, and then the dataset is classified using this tree structure [22]. K-nearest neighbors (KNN): KNN classifies new data points based on the classification of the K closest samples in the dataset in which it is located. It is generally used in technologies such as bioinformatics and fingerprint recognition. The KNN classifier is widely used, especially in the fields of big data and data mining. The KNN classifier does not require the data to be linearly separable. Also, KNN does not require any predictive model or parameter selection during the training, testing, or validation phase [23]. Logistic regression: Logistic regression is a classification algorithm. The output is given the probability that a data point belongs to one of the two classes. Usually, the classification process is performed using a linear regression model. However, linear regression is not a suitable method for classification problems because the output value is a continuous variable and cannot predict values outside the bounds. Logistic regression uses a linear regression model for classification while giving a class label in the final output [24]. Support vector machine (SVM): SVM is a learning algorithm used to classify samples in a dataset into different classes. SVM divides the samples in the dataset into 2 or more classes and selects the best-separated plane or hyperplane when classifying. SVM is a classification method that works efficiently, especially in high-dimensional feature spaces. This is because SVM uses a kernel function that moves the feature space to a low-dimensional subspace and then chooses a bounding plane to differentiate between classes [25]. Ensemble subspace KNN (ES): This method first divides the data into subfeature spaces and then creates a classification model in each subfeature space using the KNN method. The results of these submodels are then aligned and combined. The purpose of this method is to increase the classification performance by using more than one sub-model in cases where the data may behave differently in structurally different feature spaces. This technique is particularly useful as it is more scalable for higher dimensional data due to shrinking the feature space and reducing the overfitting problem [26; 27].