A Hybrid Feature Selection Method Based on Imbalanced Learning for Wave Prediction
Qinjie Lin, Xiaoli Ren, Hao Sun, Jiaming Tan, Xiaoyong Li, Jingze Lu · 2024
Wave data mining and processing are important in ocean prediction. However, wave data often exhibits imbalance, resulting in low accuracy in predicting extreme phenomena. To alleviate this problem, we propose a hybrid feature selection method based on imbalance learning (HFS-IL) to improve accuracy of prediction models. Specifically, we first use a Long Short-Term Memory (LSTM) network to train an imbalance discriminator, which aims to classify input data into common and rare subsets. Secondly, we select the optimal feature subsets by a hybrid feature selection algorithm, which is innovatively designed by combining mutual information and forward selection. To verify the effectiveness of HFS-IL, we process an imbalanced wave dataset from ERA5 by HFS-IL and use the processed data as input for an intelligent prediction model. The experimental results demonstrate that HFS-IL can effectively alleviate the impact of data imbalance and improve the accuracy of prediction, especially at station 51000, the majority of metrics outperform GRU and OSP-FEAN.