语言尸检文本分类的机器学习方法比较研究
A Comparative Study of Machine Learning Methods for Verbal Autopsy Text Classification
摘要：
口头尸检是关于未经证实的死亡情况的采访记录。在发展中国家，如果死亡发生在卫生设施之外，现场工作人员会与死者的亲属面谈死亡的情况;这种口头尸检可以在现场之外进行审查。我们报告的比较研究的过程中涉及的文本分类适用于分类死因：特征值表示;机器学习分类算法;和特征减少策略，以确定合适的方法适用于口头尸检文本的分类。我们证明了归一化的词频和标准的TFiDF在许多分类器中实现了相当的性能。实验结果还表明，支持向量机的分类性能优于其他分类算法。最后，我们证明了采用“局部半监督”特征约简策略以提高性能准确性的有效性。
英文摘要：
A Verbal Autopsy is the record of an interview about the circumstances of an uncertified death. In developing countries, if a death occurs away from health facilities, a field-worker interviews a relative of the deceased about the circumstances of the death; this Verbal Autopsy can be reviewed off-site. We report on a comparative study of the processes involved in Text Classification applied to classifying Cause of Death: feature value representation; machine learning classification algorithms; and feature reduction strategies in order to identify the suitable approaches applicable to the classification of Verbal Autopsy text. We demonstrate that normalised term frequency and the standard TFiDF achieve comparable performance across a number of classifiers. The results also show Support Vector Machine is superior to other classification algorithms employed in this research. Finally, we demonstrate the effectiveness of employing a "locally-semi-supervised" feature reduction strategy in order to increase performance accuracy.
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