Abstract
Sequence Classification has been a challenge task in recent years since sequence doesn’t have explicit features and the high-order temporal characteristics make the number of patterns extremely massive. Pattern-based classification has demonstrated its power in recent studies by mining discriminative features efficiently. Both binary and numerical discriminative features have been utilized for effective sequence classification, but the effect of each type of features hasn’t been analyzed separately. Our method selects the frequent closed unique iterative patterns as our candidate features, mined out the discriminative binary and numerical patterns for sequence classification, and given an insight into the discriminative power improvement by feature combinations. The experimental results on synthetic and real-life datasets reveal the validity of our approach.
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Du, H., Li, C., Wang, H. (2014). Mining Multiple Discriminative Patterns in Software Behavior Analysis. In: Hervás, R., Lee, S., Nugent, C., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. Personalisation and User Adapted Services. UCAmI 2014. Lecture Notes in Computer Science, vol 8867. Springer, Cham. https://doi.org/10.1007/978-3-319-13102-3_81
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DOI: https://doi.org/10.1007/978-3-319-13102-3_81
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