A Parameter-Free Approach for Mining Robust Sequential Classification Rules Article Swipe
YOU?
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· 2015
· Open Access
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· DOI: https://doi.org/10.1109/icdm.2015.87
Sequential data is generated in many domains of science and technology. Although many studies have been carried out for sequence classification in the past decade, the problem is still a challenge, particularly for pattern-based methods. We identify two important issues related to pattern-based sequence classification which motivate the present work: the curse of parameter tuning and the instability of common interestingness measures. To alleviate these issues, we suggest a new approach and framework for mining sequential rule patterns for classification purpose. We introduce a space of rule pattern models and a prior distribution defined on this model space. From this model space, we define a Bayesian criterion for evaluating the interest of sequential patterns. We also develop a parameter-free algorithm to efficiently mine sequential patterns from the model space. Extensive experiments show that (i) the new criterion identifies interesting and robust patterns, (ii) the direct use of the mined rules as new features in a classification process demonstrates higher inductive performance than the state-of-the-art sequential pattern based classifiers.
Related Topics
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1109/icdm.2015.87
- OA Status
- green
- Cited By
- 13
- References
- 57
- Related Works
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- OpenAlex ID
- https://openalex.org/W2240528180
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2240528180Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/icdm.2015.87Digital Object Identifier
- Title
-
A Parameter-Free Approach for Mining Robust Sequential Classification RulesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
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2015-11-01Full publication date if available
- Authors
-
Elias Egho, Dominique Gay, Marc Boullé, Nicolas Voisine, Fabrice ClérotList of authors in order
- Landing page
-
https://doi.org/10.1109/icdm.2015.87Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://hal.univ-reunion.fr/hal-01395002Direct OA link when available
- Concepts
-
Computer science, Sequence (biology), Data mining, Artificial intelligence, Classification rule, Process (computing), Machine learning, Bayesian probability, Sequential Pattern Mining, Pattern recognition (psychology), Operating system, Biology, GeneticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2022: 2, 2021: 1, 2020: 2, 2019: 2Per-year citation counts (last 5 years)
- References (count)
-
57Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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