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Ensemble Classification Based on Feature Selection for Environmental Sound Recognition

机译：基于特征选择的集合体分类方法在环境声音识别中的应用

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摘要

Environmental sound recognition has been a hot topic in the domain of audio recognition. How to select the optimal feature subsets and enhance the performance of classification precisely is an urgent problem to be solved. Ensemble learning, a new kind of method presented recently, has been an effective way to improve the accuracy of classification in feature selection. In this paper, experiments were performed on environmental sound dataset. An improved method based on constraint score and multimodels ensemble feature selection methods (MmEnFs) were exploited in the experiments. The experimental results show that when enough attributes are selected, the improved method can get a better performance compared to other feature selection methods. And the ensemble feature selection method, which combines other methods, can obtain the optimal performance in most cases.
机译：环境声音识别已成为音频识别领域的热门话题。如何选择最佳的特征子集并准确地提高分类性能是当前亟待解决的问题。集成学习是最近提出的一种新方法，已经成为提高特征选择中分类精度的有效方法。在本文中，对环境声音数据集进行了实验。实验中采用了一种基于约束评分和多模型集成特征选择方法（MmEnFs）的改进方法。实验结果表明，与其他特征选择方法相比，选择足够多的属性时，改进后的方法可以获得更好的性能。结合其他方法的集成特征选择方法可以在大多数情况下获得最佳性能。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第5期|4318463.1-4318463.7|共7页
  • 作者

    Zhao Shuai; Zhang Yan; Xu Haifeng; Han Te;

  • 作者单位

    Southwest Forestry Univ, Coll Big Data & Intelligence Engn, Kunming 650224, Yunnan, Peoples R China;

    Southwest Forestry Univ, Coll Big Data & Intelligence Engn, Kunming 650224, Yunnan, Peoples R China;

    Southwest Forestry Univ, Coll Big Data & Intelligence Engn, Kunming 650224, Yunnan, Peoples R China;

    Southwest Forestry Univ, Coll Big Data & Intelligence Engn, Kunming 650224, Yunnan, Peoples R China;

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