## 5 Proposed Feature Selection Method (A2 BCF)
As can be seen, setting the PS around 50 gives the algorithm the chance to improve its performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Accuracy (\%)**} \\ \hline
**Classifier** & \multicolumn{2}{c}{**Reduced Feature Set by A2 BCF**} & \multicolumn{2}{c}{**Original**} \\  & & & **Dataset**[34] \\ \hline  & **N = 20** & **N = 50** & **N = 100** & **N/A** \\ \hline RF & 88.47 & **88.51** & 88.44 & 85.30 \\ XGBoost & 88.67 & **88.76** & 88.64 & 85.16 \\ SVM & 87.84 & **88.00** & 87.81 & 85.06 \\ \hline \hline \end{tabular}

* N is the secondary population size after clustering and 3-fold cross-validation.

\end{table} 
Table 1: Comparison of A2 BCF under different population sizes with a previous study. Among the three ML classifiers leveraged to predict student success, XGBoost achieves the best accuracy (88.76%) and is the most well-performed model. Hence, XGBoost is a good candidate for the final FSO framework in this application. In summary, compared with a previous study [34] that considered the features without the FSO method, A2 BCF outperforms in predicting student success. A2 BCF uses an ABC-based approach to optimize data features and improve classification accuracy. We also compared the results with another FSO method. Table 2 provides the comparison of the result between the PSO [45] and the proposed A2 BCF method. The results show that A2 BCF performs better than PSO. Based on the obtained results, it can be concluded that by leveraging A2 BCF, we achieve better accuracy. ## 7 Discussion

In this work, an improved version of the ABC algorithm, A2 BCF, is proposed for an automated feature selection approach in different ML algorithms. The proposed method is explored through a binary classification problem and tested on an educational application to predict undergraduate student success in 20 universities across the United States. The proposed method improves the exploration capability of the basic ABC algorithm by incorporating OBL learning algorithms. Further, the clustering algorithm used in the initial phase improves the population diversity and decreases the chance of searching potential areas. The obtained results are compared with those of a recent study, and the results show the robustness of A2 BCF. The behavior of A2 BCF is explored in different \begin{table}
\begin{tabular}{c c c c} \hline \hline
**FSO Method** & \multicolumn{2}{c}{**Accuracy (\%)**} \\ \hline  & **N = 20** & **N = 50** & **N = 100** \\ \hline A2 BCF & **88.51** & **88.76** & **88.64** \\ PSO [45] & 87.88 & 87.70 & 88.00 \\ \hline \hline \end{tabular}

N is the number of food sources/particles in ABC and PSO approaches, respectively.

\end{table} 
Table 2: Comparison of A2 BCF and PSO feature selection approaches. Figure 4: Impact of population size on classifier accuracy. conditions, and experimental results show that the algorithm improves the accuracy and can be employed to solve educational problems with relatively high dimensionality. The main advantage of the proposed algorithm is its capability to find the optimal features in a reasonable time compared to exhaustive methods (such as grid search). A2 BCF could be used as an ideal tool for preprocessing that optimizes the feature selection process, as it enhances the classification accuracy and minimizes the computational resources. Additionally, in this study, we are targeting to solve a specific problem in an education application. To this end, we focus on developing a tailored FSO algorithm that improves classification accuracy to predict student success. Based on the findings, it can be concluded that A2 BCF is a promising approach for FSO to improve classification accuracy. This study is a part of an ongoing research study to develop a robust AutoML framework that works well in different applications. Some future works are planned with the following directions. First, since we are only building classification models, investigating how our method can be extended to deal with regression problems can be helpful.