Foundations of statistical natural language processing
Foundations of statistical natural language processing
Maintaining knowledge about temporal intervals
Communications of the ACM
Extended Boolean information retrieval
Communications of the ACM
Mining data streams under block evolution
ACM SIGKDD Explorations Newsletter
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Building and applying a concept hierarchy representation of a user profile
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient decision tree construction on streaming data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Word association norms, mutual information, and lexicography
ACL '89 Proceedings of the 27th annual meeting on Association for Computational Linguistics
Data Mining in Time Series Database
Data Mining in Time Series Database
A hybrid decision tree/genetic algorithm method for data mining
Information Sciences: an International Journal - Special issue: Soft computing data mining
Text Mining: Predictive Methods for Analyzing Unstructured Information
Text Mining: Predictive Methods for Analyzing Unstructured Information
On Change Diagnosis in Evolving Data Streams
IEEE Transactions on Knowledge and Data Engineering
Comparative study of name disambiguation problem using a scalable blocking-based framework
Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Finding useful fuzzy concepts for pattern classification using genetic algorithm
Information Sciences: an International Journal
A Framework for On-Demand Classification of Evolving Data Streams
IEEE Transactions on Knowledge and Data Engineering
Algorithms for time series knowledge mining
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining evolving data streams for frequent patterns
Pattern Recognition
Decision-tree instance-space decomposition with grouped gain-ratio
Information Sciences: an International Journal
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A regression-based temporal pattern mining scheme for data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Subspace based feature selection for pattern recognition
Information Sciences: an International Journal
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Multivariable stream data is becoming increasingly common as diverse types of sensor devices and networks are deployed. Building accurate classification models for such data has attracted a lot of attention from the research community. Most of the previous works, however, relied on features extracted from individual streams, and did not take into account the dependency relations among the features within and across the streams. In this work, we propose new classification models that exploit temporal relations among features. We showed that consideration of such dependencies does significantly improve the classification accuracy. Another benefit of employing temporal relations is the improved interpretability of the resulting classification models, as the set of temporal relations can be easily translated to a rule using a sequence of inter-dependent events characterizing the class. We evaluated the proposed scheme using different classification models including the Naive Bayesian, TFIDF, and vector distance models. We showed that the proposed model can be a useful addition to the set of existing stream classification algorithms.