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Indexed by:期刊论文

Date of Publication:2021-03-05

Journal:IEEE ACCESS

Volume:8

Page Number:218199-218214

ISSN No.:2169-3536

Key Words:Sequence classification; sequential data analysis; cluster analysis; hypothesis testing; sequence embedding

Abstract: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.