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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.00074 |
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| _version_ | 1866909374344069120 |
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| author | Diop, Lamine Plantevit, Marc Soulet, Arnaud |
| author_facet | Diop, Lamine Plantevit, Marc Soulet, Arnaud |
| contents | Efficient learning from streaming data is important for modern data analysis due to the continuous and rapid evolution of data streams. Despite significant advancements in stream pattern mining, challenges persist, particularly in managing complex data streams like sequential and weighted itemsets. While reservoir sampling serves as a fundamental method for randomly selecting fixed-size samples from data streams, its application to such complex patterns remains largely unexplored. In this study, we introduce an approach that harnesses a weighted reservoir to facilitate direct pattern sampling from streaming batch data, thus ensuring scalability and efficiency. We present a generic algorithm capable of addressing temporal biases and handling various pattern types, including sequential, weighted, and unweighted itemsets. Through comprehensive experiments conducted on real-world datasets, we evaluate the effectiveness of our method, showcasing its ability to construct accurate incremental online classifiers for sequential data. Our approach not only enables previously unusable online machine learning models for sequential data to achieve accuracy comparable to offline baselines but also represents significant progress in the development of incremental online sequential itemset classifiers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_00074 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RPS: A Generic Reservoir Patterns Sampler Diop, Lamine Plantevit, Marc Soulet, Arnaud Machine Learning Artificial Intelligence Combinatorics Probability 60: Probability theory G.3; E.1; E.2; F.2 Efficient learning from streaming data is important for modern data analysis due to the continuous and rapid evolution of data streams. Despite significant advancements in stream pattern mining, challenges persist, particularly in managing complex data streams like sequential and weighted itemsets. While reservoir sampling serves as a fundamental method for randomly selecting fixed-size samples from data streams, its application to such complex patterns remains largely unexplored. In this study, we introduce an approach that harnesses a weighted reservoir to facilitate direct pattern sampling from streaming batch data, thus ensuring scalability and efficiency. We present a generic algorithm capable of addressing temporal biases and handling various pattern types, including sequential, weighted, and unweighted itemsets. Through comprehensive experiments conducted on real-world datasets, we evaluate the effectiveness of our method, showcasing its ability to construct accurate incremental online classifiers for sequential data. Our approach not only enables previously unusable online machine learning models for sequential data to achieve accuracy comparable to offline baselines but also represents significant progress in the development of incremental online sequential itemset classifiers. |
| title | RPS: A Generic Reservoir Patterns Sampler |
| topic | Machine Learning Artificial Intelligence Combinatorics Probability 60: Probability theory G.3; E.1; E.2; F.2 |
| url | https://arxiv.org/abs/2411.00074 |