Open Access
Subscription Access
GBRUN: A Gradient Search-based Binary Runge Kutta Optimizer for Feature Selection
Abstract
Feature selection (FS) is a pre-processing technique for data dimensionality reduction in machine learning and data mining algorithms. FS technique reduces the number of features and improves the model generalization ability. This study presents a Gradient Search-based Binary Runge Kutta Optimizer (GBRUN) for solving the FS problem of high-dimensional. First, the proposed method converts the continuous Runge Kutta optimizer (RUN) into a binary version through S-, V-, and U-shaped transfer functions. Second, a gradient search method is introduced to improve the exploration capability of the algorithm. Five standard datasets provided by Arizona State University’s Data Mining and Machine Learning Lab were selected to verify the performance of the GBRUN algorithm. The experimental results show that GBRUN has better performance than other advanced algorithms regarding classification accuracy and the number of selected features. Moreover, the GBRUN algorithm is also combined with EfficientNet in this manuscript, using the GBRUN algorithm to select the features extracted by EfficientNet. The results show that the V-shaped (GBRUN-V) and U-shaped (GBRUN-U) algorithms have better performance than other algorithms.
Keywords
GBRUN, Feature selection, Runge Kutta method, COVID-19 dataset, EfficientNet
Citation Format:
Zhi-Chao Dou, Shu-Chuan Chu, Zhongjie Zhuang, Ali Riza Yildiz, Jeng-Shyang Pan, "GBRUN: A Gradient Search-based Binary Runge Kutta Optimizer for Feature Selection," Journal of Internet Technology, vol. 25, no. 3 , pp. 341-353, May. 2024.
Zhi-Chao Dou, Shu-Chuan Chu, Zhongjie Zhuang, Ali Riza Yildiz, Jeng-Shyang Pan, "GBRUN: A Gradient Search-based Binary Runge Kutta Optimizer for Feature Selection," Journal of Internet Technology, vol. 25, no. 3 , pp. 341-353, May. 2024.
Full Text:
PDFRefbacks
- There are currently no refbacks.
Published by Executive Committee, Taiwan Academic Network, Ministry of Education, Taipei, Taiwan, R.O.C
JIT Editorial Office, Office of Library and Information Services, National Dong Hwa University
No. 1, Sec. 2, Da Hsueh Rd., Shoufeng, Hualien 974301, Taiwan, R.O.C.
Tel: +886-3-931-7314 E-mail: jit.editorial@gmail.com