Abstract
This paper introduces a new family of string classifiers based on local relatedness. We use three types of local relatedness measurements, namely, longest common substrings (LCStr’s), longest common subsequences (LCSeq’s), and window-accumulated longest common subsequences (wLCSeq’s). We show that finding the optimal classier for given two sets of strings (the positive set and the negative set), is NP-hard for all of the above measurements. In order to achieve practically efficient algorithms for finding the best classifier, we investigate pruning heuristics and fast string matching techniques based on the properties of the local relatedness measurements.
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Higa, Y., Inenaga, S., Bannai, H., Takeda, M. (2006). A New Family of String Classifiers Based on Local Relatedness. In: Todorovski, L., Lavrač, N., Jantke, K.P. (eds) Discovery Science. DS 2006. Lecture Notes in Computer Science(), vol 4265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893318_14
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DOI: https://doi.org/10.1007/11893318_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46491-4
Online ISBN: 978-3-540-46493-8
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