Time-sensitive feature mining for temporal sequence classification

Authors:
Yong Yang;Longbing Cao;Li Liu
Affiliations:
Data Sciences & Knowledge Discovery Lab, Center for Quantum Computation and Intelligent Systems, Faculty of Engineering & Information Technology, University of Technology, Sydney;Data Sciences & Knowledge Discovery Lab, Center for Quantum Computation and Intelligent Systems, Faculty of Engineering & Information Technology, University of Technology, Sydney;Data Sciences & Knowledge Discovery Lab, Center for Quantum Computation and Intelligent Systems, Faculty of Engineering & Information Technology, University of Technology, Sydney
Venue:
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Year:
2010

Citing 11
Cited 0

Mining features for sequence classification

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Scaling up dynamic time warping for datamining applications

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences

Machine Learning
Learning Logical Definitions from Relations

Machine Learning
Implicit motif distribution based hybrid computational kernel for sequence classification

Bioinformatics
Experiencing SAX: a novel symbolic representation of time series

Data Mining and Knowledge Discovery
Behavior Informatics and Analytics: Let Behavior Talk

ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
Principal Manifolds for Data Visualization and Dimension Reduction

Principal Manifolds for Data Visualization and Dimension Reduction
Protein sequence classification through relevant sequence mining and bayes classifiers

EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
DNA sequence classification via an expectation maximizationalgorithm and neural networks: a case study

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Orthogonal Laplacianfaces for Face Recognition

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Behavior analysis received much attention in recent year, such as customer-relationship management, social security surveillance and e-business. Discovering high impact-driven behavior patterns is important for detecting and preventing their occurrences and reducing resulting risks and losses to our society. In data mining community, researchers pay little attention to time-stamps in temporal behavior sequences (without explicitly considering inherent temporal information) during classification. In this paper, we propose a novel Temporal Feature Extraction Method - TFEM. It extracts sequential pattern features where each transition is annotated with a typical transition time (its duration or interval). Therefore it substantially enriches temporal characteristics derived from temporal sequences, yielding improvements in performances, as demonstrated by a set of experiments performed on synthetic and real-world datasets. In addition, TFEM has the merit of simplicity in implementation and its pattern-based architecture can generate human-readable results and supply clear interpretability to users. Meanwhile, it is adjustable and adaptive to user's different configurations, allowing a tradeoff between classification accuracy and time cost.