Feature Generation for Sequence Categorization

The problem of sequence categorization is to generalize from a corpus of labeled sequences procedures for accurately labeling future unlabeled sequences. The choice of representation of sequences can have a major impact on this task, and in the absence of background knowledge a good representation is often not knownand straightforward representations are often far from optimal. Wepropose a feature generation method (called FGEN)that creates Boolean features that check for the presence or absence of heuristically selected collections of subsequences. Weshow empirically that the representation computedby FGEN improves the accuracy of two commonly used learning systems (C4. 5 and Ripper) whenthe new features are added to existing representations of sequence data. Weshowthe superiority of FGEN across a range of tasks selected from three domains: DNAsequences, Unix commandsequences, and English text.

Authors

• Daniel Kudenko University of York

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