Learning from String Sequences
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910441975840768 |
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| author | Lindsay, David Lindsay, Sian |
| author_facet | Lindsay, David Lindsay, Sian |
| contents | The Universal Similarity Metric (USM) has been demonstrated to give practically useful measures of "similarity" between sequence data. Here we have used the USM as an alternative distance metric in a K-Nearest Neighbours (K-NN) learner to allow effective pattern recognition of variable length sequence data. We compare this USM approach with the commonly used string-to-word vector approach. Our experiments have used two data sets of divergent domains: (1) spam email filtering and (2) protein subcellular localization. Our results with this data reveal that the USM-based K-NN learner (1) gives predictions with higher classification accuracy than those output by techniques that use the string-to-word vector approach, and (2) can be used to generate reliable probability forecasts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_06301 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning from String Sequences Lindsay, David Lindsay, Sian Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science Computation and Language Computer Vision and Pattern Recognition The Universal Similarity Metric (USM) has been demonstrated to give practically useful measures of "similarity" between sequence data. Here we have used the USM as an alternative distance metric in a K-Nearest Neighbours (K-NN) learner to allow effective pattern recognition of variable length sequence data. We compare this USM approach with the commonly used string-to-word vector approach. Our experiments have used two data sets of divergent domains: (1) spam email filtering and (2) protein subcellular localization. Our results with this data reveal that the USM-based K-NN learner (1) gives predictions with higher classification accuracy than those output by techniques that use the string-to-word vector approach, and (2) can be used to generate reliable probability forecasts. |
| title | Learning from String Sequences |
| topic | Machine Learning Artificial Intelligence Computational Engineering, Finance, and Science Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.06301 |