Learning From String Sequences

Abstract. The Universal Similarity Metric (USM) has been demon-strated to give practically useful measures of “similarity ” between se-quence data. Here we have used the USM as an alternative distance metric in a K-Nearest Neighbours (K-NN) learner to allow effective pat-tern 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 e-mail filtering and (2) protein subcellular localisation. 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.