The nature of statistical learning theory
The nature of statistical learning theory
Sequence mining in categorical domains: incorporating constraints
Proceedings of the ninth international conference on Information and knowledge management
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
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Localization Site Prediction for Membrane Proteins by Integrating Rule and SVM Classification
IEEE Transactions on Knowledge and Data Engineering
Non-redundant sequential rules-Theory and algorithm
Information Systems
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In this paper we present a new classifier based on sequential classification rules for protein localization prediction. We also present three compact representations for encoding, in a concise form, the knowledge available in a classification rule set. Experiments run on the Gram-bacteria data set show that the classifier achieves both high prediction and good recall. Furthermore, since rules can be easily interpreted, biologists can understand classification results. To further improve classification performance, an SVM classifier is used to process data not covered by means of the sequential rule classifier.