篇名 |
GBRUN: A Gradient Search-based Binary Runge Kutta Optimizer for Feature Selection
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並列篇名 | GBRUN: A Gradient Search-based Binary Runge Kutta Optimizer for Feature Selection |
作者 | Zhi-Chao Dou、Shu-Chuan Chu、Zhongjie Zhuang、Ali Riza Yildiz、Jeng-Shyang Pan |
英文摘要 | 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.
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起訖頁 | 341-353 |
關鍵詞 | GBRUN、Feature selection、Runge Kutta method、COVID-19 dataset、EfficientNet |
刊名 | 網際網路技術學刊 |
期數 | 202405 (25:3期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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