A2BCF: An Automated ABC-Based Feature Selection Algorithm for Classification Models in an Education Application

Applied Sciences

Farid Ghareh Mohammadi¹  Leila Zahedi²  Mohammad Hadi Amini² 

[1] Department of Computer Science, University of Georgia, Athens, GA 30602, USA;Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USA;

关键词: AutoML;    Artificial Bee Colony;    classification;    education;    evolutionary computation;    feature selection;   

DOI  :  10.3390/app12073553

来源: DOAJ

【 摘 要 】

Feature selection is an essential step of preprocessing in Machine Learning (ML) algorithms that can significantly impact the performance of ML models. It is considered one of the most crucial phases of automated ML (AutoML). Feature selection aims to find the optimal subset of features and remove the noninformative features from the dataset. Feature selection also reduces the computational time and makes the data more understandable to the learning model. There are various heuristic search strategies to address combinatorial optimization challenges. This paper develops an Automated Artificial Bee Colony-based algorithm for Feature Selection (A2BCF) to solve a classification problem. The application domain evaluating our proposed algorithm is education science, which solves a binary classification problem, namely, undergraduate student success. The modifications made to the original Artificial Bee Colony algorithm make the algorithm a well-performed approach.

【 授权许可】

Unknown