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Development of a noise sources classification system based on new method for feature selection

机译：基于新特征选择方法的噪声源分类系统开发

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摘要

Feature selection of noise sources is important for noise sources detection and classification. In this paper, a new rough set based feature selection method has been given. Based on the method, a noise sources automatic classification system (NSACS) has been designed and validated. The key idea of the method is that most effective features can distinguish the most number of samples belonging to different classes of noise sources, if they are used for classification. This new approach has been applied into the system NSACS to select relevant features for artificial datasets and real-world datasets and the results have shown that this approach can correctly select all the relevant features of artificial datasets and at the same time it can drastically reduce the number of features. From the experiments, it can be found that to consider all the five datasets, the number of classification features after selection drops to 35% and the accurate classification rate increases about 14%. For the underwater noise sources dataset the number of features drops to 1/5 and the accurate classification rate increases about 6% after feature selection.
机译：噪声源的特征选择对于噪声源的检测和分类很重要。本文提出了一种新的基于粗糙集的特征选择方法。基于该方法，设计并验证了噪声源自动分类系统（NSACS）。该方法的关键思想是，如果将最有效的特征用于分类，则可以区分出属于不同类别噪声源的最多数量的样本。该新方法已被应用到系统NSACS中，以选择人工数据集和真实数据集的相关特征，结果表明，该方法可以正确选择人工数据集的所有相关特征，同时可以大幅度减少人工数据集的特征。功能数量。从实验中可以发现，考虑所有五个数据集，选择后的分类特征数量下降到35％，准确分类率提高约14％。对于水下噪声源数据集，特征数量下降到1/5，选择特征后，准确的分类率提高约6％。

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