Volume 38, Number 3, June 2020
|Page(s)||471 - 477|
|Published online||06 August 2020|
Feature Selection on Maximum Information Coefficient for Underwater Target Recognition
School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
2 School of Electronic Information and Artificial Intelligence, Shaanxi University of Science and Technology, Xi'an 710021, China
Feature selection is an essential process in the identification task because the irrelevant and redundant features contained in the unselected feature set can reduce both the performance and efficiency of recognition. However, when identifying the underwater targets based on their radiated noise, the diversity of targets, and the complexity of underwater acoustic channels introduce various complex relationships among the extracted acoustic features. For this problem, this paper employs the normalized maximum information coefficient (NMIC) to measure the correlations between features and categories and the redundancy among different features and further proposes an NMIC based feature selection method (NMIC-FS). Then, on the real-world dataset, the average classification accuracy estimated by models such as random forest and support vector machine is used to evaluate the performance of the NMIC-FS. The analysis results show that the feature subset obtained by NMIC-FS can achieve higher classification accuracy in a shorter time than that without selection. Compared with correlation-based feature selection, laplacian score, and lasso methods, the NMIC-FS improves the classification accuracy faster in the process of feature selection and requires the least acoustic features to obtain classification accuracy comparable to that of the full feature set.
Key words: feature selection / ship-radiated noise / maximum correlation coefficient
关键字 : 特征选择 / 舰船辐射噪声 / 最大相关系数
© 2019 Journal of Northwestern Polytechnical University. All rights reserved.
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