Efficient evaluation of large sequence kernels
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
2D similarity kernels for biological sequence classification
Proceedings of the 11th International Workshop on Data Mining in Bioinformatics
Biological Sequence Classification with Multivariate String Kernels
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
| Hi-index | 0.00 |
We present a general, simple feature representation of sequences that allows efficient inexact matching, comparison and classification of sequential data. This approach, recently introduced for the problem of biological sequence classification, exploits a novel multi-scale representation of strings. The new representation leads to discovery of very efficient algorithms for string comparison, independent of the alphabet size. We show that these algorithms can be generalized to handle a wide gamut of sequence classification problems in diverse domains such as the music and text sequence classification. The presented algorithms offer low computational cost and highly scalable implementations across different application domains. The new method demonstrates order-of-magnitude running time improvements over existing state-of-the-art approaches while matching or exceeding their predictive accuracy.