A Review of Feature Selection Method Based on Optimization Algorithms

محورهای موضوعی : Journal of Computer & Robotics

Zohre Sadeghian ¹ , Ebrahim Akbari ² , Hossein Nematzadeh ³ , Homayun Motameni ⁴

1 - Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
2 - Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
3 - Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran
4 - Department of Computer Engineering, Sari Branch, Islamic Azad University, Sari, Iran

تاریخ دریافت : 1401/09/19 تاریخ پذیرش : 1401/10/02 تاریخ انتشار : 1401/10/11

کلید واژه: Classification, Feature Selection, Meta-Heuristic Algorithms, Optimization algorithm, Data dimension reduction,

چکیده مقاله :

Feature selection is the process of identifying relevant features and removing irrelevant and repetitive features with the aim of observing a subset of features that describe the problem well and with minimal loss of efficiency. One of the feature selection approaches is using optimization algorithms. This work provides a summary of some meta-heuristic feature selection methods proposed from 2018 to 2021 that were designed and implemented on a wide range of different data. The results of the study showed that some meta-heuristic algorithms alone cannot perfectly solve the feature selection problem on all types of datasets with an acceptable speed. In other words, depending on dataset, a special meta-heuristic algorithm should be used.