Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
Feature generation for sequence categorization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Mining features for sequence classification
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Growing decision trees on support-less association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Web site mining: a new way to spot competitors, customers and suppliers in the world wide web
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Frequent-subsequence-based prediction of outer membrane proteins
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Frequent Closed Sequence Mining without Candidate Maintenance
IEEE Transactions on Knowledge and Data Engineering
CONTOUR: an efficient algorithm for discovering discriminating subsequences
Data Mining and Knowledge Discovery
Multi-represented classification based on confidence estimation
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
A brief survey on sequence classification
ACM SIGKDD Explorations Newsletter
Efficient Mining of Gap-Constrained Subsequences and Its Various Applications
ACM Transactions on Knowledge Discovery from Data (TKDD)
Multi-represented kNN-classification for large class sets
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Sentiment classification with supervised sequence embedding
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Self-Organizing Hidden Markov Model Map (SOHMMM)
Neural Networks
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In recent years we have witnessed an exponential increase in the amount of biological information, either DNA or protein sequences, that has become available in public databases. This has been followed by an increased interest in developingcomp utational techniques to automatically classify these large volumes of sequence data into various categories corresponding to either their role in the chromosomes, their structure, and/or their function. In this paper we evaluate some of the widely-used sequence classification algorithms and develop a framework for modeling sequences in a fashion so that traditional machine learning algorithms, such as support vector machines, can be applied easily. Our detailed experimental evaluation shows that the SVM-based approaches are able to achieve higher classification accuracy compared to the more traditional sequence classification algorithms such as Markov model based techniques and K-nearest neighbor based approaches.