## 4 Results and Discussion  
In this section, the experimental results of the proposed FL-BDE model for detecting Android malware and nine different ML- and EL-based methods used to verify the model's performance are presented. The success of the proposed model in malware detection is proven by comparing the experimental results with the studies in the literature that use similar models and techniques. The FL-BDE model's experimental setup was built in the Microsoft Azure ML environment using the components that are classified into several categories in the Materials and Methods section (Chapter 3). In the first phase of the setup, the data were read from the csv file containing 2000 applications, with a total of 1134 features, which had been uploaded to the Azure ML platform through the \"My Datasets\" component. The relevant data were therefore applied to the \"Filter Based Feature Selection\" component and reduced to a 2000 \(\times\) 50 dataset. The \"Feature Selection Method\" and \"Number of Desired Features\" parameters in this component were set to the \"Fisher score\" and \"50\", respectively. To obtain the training and testing data for the ML-based models, the dataset with the decreased feature set was then subject to the data splitting procedure. The Azure ML environment's \"Split Data\" component was used to carry out this process. Depending on the two-stage splitting strategy that had to be employed in the test phase, the \"fraction of rows in the first output dataset\" parameter, which displays the split ratio within the component, was set to 0.60 or 0.70, respectively. Three components, \"Two Class Algorithm Name,\" \"Train Model\" and \"Score Model\", were used to produce the malicious rating results in the 0-1 range of the models developed using the SVM, LR, BPM, BDT, DF and NN approaches utilizing the training and test data for the multi-classification process. The parameters adjusted for the mentioned classification algorithms and their value changes are shown in Table 2.  
The stages, including the voting and routing processes and the combining scores process of the proposed model, with the execution of the single classifiers that are not part of Azure machine learning, were implemented with a program written on the \"Execute R script\" component in this environment. Along with the proposed FL-BDE model in the experimental studies, the ML-based single classifiers such as SVM, LR, BPM, NN and RF and the EL-based single classifiers such as BDT, DF, AdaBoost and Bagging were also used to evaluate its performance in Android malware detection. The performances of the model and classifiers were verified on the training and testing data obtained by applying the random split and k-fold cross-validation conditions to the dataset. Splits of 0.60 and 0.70 were used as the random split condition. Data vectors of 1200 \(\times\) 50 for the training process and 800 \(\times\) 50 for the testing process were obtained in the random split condition of 0.60. For the split ratio of 0.70, the sizes of these vectors for the training and testing processes were 1400 \(\times\) 50 and 600 \(\times\) 50, respectively. In the cross-validation approach, five-fold cross-validation was used. Firstly, the dataset was divided into five equal parts, and five data groups were obtained, each consisting of 400 \(\times\) 50 vectors. Then, after performing five iterative test processes, where there was one group of testing data and four groups of training data, the performance results of the classification models were obtained for each iteration step. After that, by averaging the performance results obtained in these iteration steps, the overall performance of the model and methods used for the Android malware detection was determined. In Table 3, the classification errors and confusion matrix parameter values for the FL-BDE, ML- and EL-based models are given for the split ratio of 0.60.