A brief survey on sequence classification
A brief survey on sequence classification
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DOI：
10.1145/1882471.1882478
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发表时间：
2010-11
期刊：
影响因子：
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通讯作者：
Zhengzheng Xing;J. Pei;Eamonn J. Keogh
中科院分区：
文献类型：
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作者：
Zhengzheng Xing;J. Pei;Eamonn J. Keogh
序列分类具有广泛的应用，例如基因组分析、信息检索、健康信息学、金融和异常检测。与基于特征向量的分类任务不同，序列不具有显式特征。即使使用复杂的特征选择技术，潜在特征的维度可能仍然非常高，并且难以捕获特征的顺序性质。这使得序列分类比特征向量分类更具挑战性。在本文中，我们提出了一个简短的评论现有的工作序列分类。我们总结了序列分类的方法和应用领域。我们还提供了一个评论的几个扩展的序列分类问题，如早期分类序列和半监督学习序列。
Sequence classification has a broad range of applications such as genomic analysis, information retrieval, health informatics, finance, and abnormal detection. Different from the classification task on feature vectors, sequences do not have explicit features. Even with sophisticated feature selection techniques, the dimensionality of potential features may still be very high and the sequential nature of features is difficult to capture. This makes sequence classification a more challenging task than classification on feature vectors. In this paper, we present a brief review of the existing work on sequence classification. We summarize the sequence classification in terms of methodologies and application domains. We also provide a review on several extensions of the sequence classification problem, such as early classification on sequences and semi-supervised learning on sequences.