A Robust Approach to Sequence Classification

Authors:
Ming Li;Ronan Sleep
Affiliations:
University of East Anglia;University of East Anglia
Venue:
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Year:
2005

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Abstract

We report results for classification of representations of music, spoken words, and text documents. Experimental comparisons with other state-of-the-art algorithms yield improved results for all three examples. We use a Support Vector Machine (SVM) as our classifier in all experiments. This is driven by a kernel matrix of similarity measures between the sequences. Our similarity measure is based on n-grams of varying length (multi-grams), weighted to reflect discrimination ability. To alleviate the problem of the exponential growth of feature size with n, we use a modified LZ78 algorithm [1] to guide feature selection. Our method exhibits good performance over the three widely distinct tasks reported here, and is very computationally efficient and may therefore be useful in real time applications.