A fuzzy inductive learning strategy for modular rules
Fuzzy Sets and Systems
Multidimensional curve classification using passing—through regions
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Learning Comprehensible Descriptions of Multivariate Time Series
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning First Order Logic Time Series Classifiers: Rules and Boosting
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Pattern Extraction for Time Series Classification
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Supervised classification with temporal data
Supervised classification with temporal data
Supervised Clustering " Algorithms and Benefits
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Exact indexing of dynamic time warping
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
| Hi-index | 0.00 |
Nowadays with time series accounting for an increasingly large fraction of world's supply of data, there has been an explosion of interest in mining time series data. This paper proposes a multivariate time series classification model which is both effective in classifier's accuracy and comprehensibility. It is composed of two stages: a supervised clustering for pattern extraction and soft discretization decision forest. In supervised clustering, some real time series instances from the training dataset will be selected as class dedicated patterns. While in decision forest, the rule induction helps to improve the knowledge acquisition of the classifier. In addition, soft discretization would further improve the accuracy and comprehensibility of the classifier.