Comparison of Genetic Algorithm and Recursive Feature Elimination on High Dimensional Data

Comparison of Genetic Algorithm and Recursive Feature Elimination on High Dimensional Data

feature selection;high-dimensional data;recursive feature elimination;genetic algorithm

The use of big data in companies is currently used in file processing. With large capacity files, it can affect the performance in terms of time in the company so to overcome the problem of high-dimensional data, feature selection is used in selecting the number of features. On the wdbc dataset with 30 features and 569 data, feature selection is performed using the Recusive Feature Elimination (RFE) and Genetic Algorithm (GA) models. Then a comparison of evaluation values is made to determine which feature selection is best for solving the problem. From the 14 tables of evaluation results and discussion in tables 1 to 14, it is found that in the evaluation of accuracy, and the use of weighted macros on precision, recall and f1 score, using GA selection features has slightly higherresults than RFE so it is concluded that GA selection features are better at solving problems in high-dimensional data

Yoga Pristyanto1, Dipa Wirantanu

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5375/916

Departmentof Information System, Facultyof Computer Science, UniversitasAmikom, Yogyakarta, Indonesia

29-03-2025

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