Machine Learning - Special issue on applications in molecular biology
Mining features for sequence classification
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth
ICDE '01 Proceedings of the 17th International Conference on Data Engineering
Frequent-subsequence-based prediction of outer membrane proteins
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Data & Knowledge Engineering
Boosting Relational Sequence Alignments
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Gradient boosting for sequence alignment
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Learning interpretable SVMs for biological sequence classification
RECOMB'05 Proceedings of the 9th Annual international conference on Research in Computational Molecular Biology
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In recent years, early prediction for ongoing sequences has been more and more valuable in a large variety of time-critical applications which demand to classify an ongoing sequence in its early stage. There are two challenging issues in early prediction, i.e. why an ongoing sequence is early predictable and how to reasonably determine the parameter koptimal, the minimum number of elements that must be observed before an accurate classification can be made. To address these issues, this paper investigates the kinetic regularity of the information transfer in sequence data set. As a result, a new concept of Accumulatively Transferred Information (ATI) and its kinetic model in early predictable sequences are proposed. This model shows that the information transfer in early predictable sequences follows Inverse Heavy-tail Distribution( IHD), and the most uncertainty of an early predictable sequence is eliminated by only few of its preceding elements, which is exactly the intrinsic and theoretically sound ground of the feasibility of early prediction. Based on the kinetic model, a heuristic algorithm is proposed to learn the parameter koptimal. The experiments are conducted on real data sets and the results validate the reasonableness and effectiveness of the proposed theory and algorithm.