## 5 Proposed Feature Selection Method (A2 BCF)
Another suggestion is to evaluate the algorithm's efficacy on other datasets, which opens the opportunity to explore how different applications affect the performance of the A2 BCF. Moreover, additional effort is needed to further improve the tuning time of such methods, including the A2 BCF, without decreasing the achieved performance. Last but not least, we plan to insert the implementation of these modifications into our hyperparameter optimization frameworks [32; 34; 35] to further improve ML algorithms' performance in this study. As mentioned earlier, the MIDFIELD dataset used is considered for predicting computing students' attrition rates in the computing field. Although the MIDFIELD dataset had been collected to denote the graduation rate of students and not for learning and teaching styles, our proposed approach is an automated and intelligent approach that can partly help education communities to provide guidelines for teaching and learning strategies. ## 8 Conclusions

Determining the optimum features is a crucial task in AutoML. In this work, the high-dimensional vector of 91 features from the combination of different features are reduced to fewer configurations using A2 BCF and achieving the 88% classification accuracy. The A2 BCF algorithm with different conventional ML classifiers is applied to the feature set to obtain the optimal or close to the optimal set of features. The results show that A2 BCF yields better performance, with 88.76% classification accuracy compared with the accuracy over the original dataset and the PSO method. L.Z. : Conceptualization, methodology, software, validation, formal analysis, investigation, data curation, writing--original draft preparation. F.G.M. : Conceptualization, review & editing, M.H.A. : Conceptualization, resources, review & editing, methodology, supervision, project administration. All authors have read and agreed to the published version of the manuscript. This research is supported through FIU's Dissertation Year Fellowships (DYF) funding. Not Applicable. The authors acknowledge the University Graduate School (UGS) at Florida International University for providing financial support for this research in the form of Dissertation Year Fellowships (DYF) for [PERSON]. The authors would also like to gratefully acknowledge [PERSON] from Purdue University for providing access to the MIDFIELD dataset and the anonymous reviewers for the constructive comments for further improving the content of this article. The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. ## Abbreviations

The following abbreviations are used in this manuscript:

A2 BCF Automated Artificial Bee Colony for Feature Selection

ABC Artificial Bee Colony

ACO Ant Colony Optimization

CV Cross Validation

FSO Feature Selection Optimization

GA Genetic Algorithm

HPC High-Performance Computational

HPO Hyperparameter Optimization

ML Machine Learning

RF Random Forest

MIDTFIELD Multiple-Institution Database for Investigating Engineering Longitudinal Development

OBL Opposition-Based Learning

PS Population Size

PSO Particle Swarm Optimization

SBS Sequential Backward Selection

SFS Sequential Forward Selection

SI Swarm Intelligence

SVM Support Vector Machine

XGBoost Extreme Gradient Boosting

## References

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