Learning from String Sequences
<|reference_start|>Learning from String Sequences: 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.<|reference_end|>
@article{lindsay2024learning,
    title={Learning from String Sequences},
    author={David Lindsay and Sian Lindsay},
    journal={arXiv preprint arXiv:2405.06301},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06301},
    primaryClass={cs.LG cs.AI cs.CE cs.CL cs.CV}
}