## 5 Proposed Feature Selection Method (A2 BCF)
Therefore, finding patterns in historical data can help education researchers and computing committees to detect the students who are more likely to drop out of their programs. MIDFIELD is a unit-record longitudinal database for bachelors' students from 20 universities across the United States. This version of the MIDFIELD dataset is used for a binary classification problem and has 4532 samples with 91 features after one-hot encoding the features. This dataset includes students' GPA (Grade Point Average), CIP (Classification of Instructional Programs code, standing for the students' major during the term, which is expressed as IPEDS (Integrated Postsecondary Education Data System), and COOP (Co-Operative education program) indicating student's participation in a partnership between their academic institution and an employer to obtain practical experience through rotations of course study and employment [43]. Further, the SAT (Scholarship Aptitude Test) and the ACT (American College Testing) are standardized tests used for college admissions in the United States. Additionally, demographic information of the students, such as age, gender, race, and citizenship, is also included in this dataset. Information such as institution, transfer status, and the terms registered in the program are also among the dataset features. In summary, MIDFIELD consists of all the students' information in their universities profiles. All the data are preprocessed using the techniques explained in the previous section. The dataset features are taken as the input for the A2 BCF algorithm. To evaluate the fitness function, we use three classifiers, and ABC parameters are set to Maximum-iterations = 100; Initial Population Size (PS) = 2000, 5000, and 10,000; k = 100, with an average run of 10 times. The experiment is performed using cross-validation (CV) to yield more robust results. CV (3-fold) splits the input data into training data and test data independent of each other. Although cross-validation may increase the computational time, it reduces the chances of overfitting and provides a more reliable model. In this study, we used parallel cross-validation, and the final accuracy would be an average of the accuracy for each of the folds. We used _Scikit-learn_ libraries, along with other Python packages, to leverage the ML models. The study experiments were conducted using Python 3.8.5 on High-Performance Computational (HPC) resources. ### Results

This section covers the final results using the proposed algorithm. The validating process of the suggested A2 BCF algorithm was executed on MIDFIELD dataset [42, 44]. We compare the performance of A2 BCF with a previous study using the same number of folds as in CV (3-fold) [34] while using the MIDFIELD dataset to answer the same classification question. In summary, the proposed algorithm removes irrelevant features from the dataset in an effort to improve the accuracy. Reducing the features in this step can help the training process, training time, and/or other iterative processes such as HPO as it reduces the structure's size. In addition, diversifying the population improves the algorithm's convergence rate. The overall numerical results with the experimental setting of Section 6.3 are given in Table 1. According to the Table, in all cases, the classifiers' accuracy after applying the A2 BCF algorithm improved. As mentioned earlier, the accuracy is tested under different populations for three different classifiers (RF, XGBoost, and SVM). The advantage of A2 BCF is notable when the PS is relatively small (PS = 50). Figure 4 shows the impact of the PS on the accuracy of the three ML algorithms. As can be seen, the accuracy of classifiers in all three cases are at their highest when the PS equals 50, and it again drops when the PS increases. A potential reason is that the algorithm's number of iterations is lower when the stopping criteria (maximum number of evaluations) are met, and the algorithms do not have the chance to improve themselves so many times.