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论文类型：期刊论文

发表时间：2021-03-05

发表刊物：IEEE ACCESS

卷号：8

页面范围：218199-218214

ISSN号：2169-3536

关键字：Sequence classification; sequential data analysis; cluster analysis; hypothesis testing; sequence embedding

摘要：Sequence classification is an important data mining task in many real-world applications. Over the past few decades, many sequence classification methods have been proposed from different aspects. In particular, the pattern-based method is one of the most important and widely studied sequence classification methods in the literature. In this paper, we present a reference-based sequence classification framework, which can unify existing pattern-based sequence classification methods under the same umbrella. More importantly, this framework can be used as a general platform for developing new sequence classification algorithms. By utilizing this framework as a tool, we propose new sequence classification algorithms that are quite different from existing solutions. Experimental results show that new methods developed under the proposed framework are capable of achieving comparable classification accuracy to those state-of-the-art sequence classification algorithms.