Audio event classification using binary hierarchical classifiers with feature selection for healthcare applications
Audio event classification using binary hierarchical classifiers with feature selection for healthcare applications
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DOI：
10.1109/iscas.2008.4542148
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发表时间：
2008-05
期刊：
影响因子：
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通讯作者：
Ya-Ti Peng;Ching-Yung Lin;Ming-Ting Sun
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文献类型：
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作者：
Ya-Ti Peng;Ching-Yung Lin;Ming-Ting Sun
在本文中，提出了一种二进制分层分类器与特征选择的多类音频事件分类的医疗保健应用。在构造二元层次分类器时，我们将层次聚类和特征选择问题结合起来考虑。所提出的方法的分类器结构，以及一个紧凑的特征子集，为每个组件分类器，用于构建整体的二进制分层分类。在我们的实验中，支持向量机（SVM）的组件分类器，从几个关键的音频事件的老年人护理应用程序的分类结果显示出竞争力的性能，传统的一对一的方法，而在我们提出的计划的训练和测试SVM的数量较少。此外，特征选择通过过滤掉可能的冗余和不相关的特征成分来促进成分分类器的训练。
In this paper, a binary hierarchical classifier with feature selection is proposed for multi-class audio event classification for healthcare applications. We consider the hierarchical clustering and the feature selection problems jointly when building a binary hierarchical classifier. The proposed method results in the classifier structure as well as a compact feature subset for each component classifier for constructing the overall binary hierarchical classifier. With Support Vector Machine (SVM) for the component classifiers in our experiment, results from classifying several key audio events for the eldercare application show competitive performance to the traditional one-against-one method while the number of training and testing SVM is less in our proposed scheme. Moreover, feature selection facilitates the training of the component classifier by filtering out possible redundant and irrelevant feature components.