Reference-Based Sequence Classification

Autor: Quan Zou, Bo Xu, Guangyao Xu, Zengyou He, Chaohua Sheng
Jazyk: angličtina
Rok vydání: 2020
Předmět:
FOS: Computer and information sciences
Computer Science - Machine Learning
General Computer Science
Computer science
Sequence classification
Machine Learning (stat.ML)
02 engineering and technology
Machine Learning (cs.LG)
Task (project management)
Statistics - Machine Learning
020204 information systems
hypothesis testing
0202 electrical engineering
electronic engineering
information engineering

General Materials Science
Statistical hypothesis testing
Sequence (medicine)
business.industry
sequence embedding
General Engineering
Pattern recognition
Statistical classification
ComputingMethodologies_PATTERNRECOGNITION
sequential data analysis
020201 artificial intelligence & image processing
Artificial intelligence
lcsh:Electrical engineering. Electronics. Nuclear engineering
business
lcsh:TK1-9971
cluster analysis
Zdroj: IEEE Access, Vol 8, Pp 218199-218214 (2020)
ISSN: 2169-3536
Popis: 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.
Databáze: OpenAIRE
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