Paper Info
Title
A Discriminative Shapelets Transformation For Time Series Classification
Abstract
Time series shapelets are subsequences of time series that could be representative of a class. Shapelets-based time series classification methods can be divided into two large categories. The first category integrates shapelets selection within the process of constructing classifier; while the second category disconnects the process of finding shapelets from the classification algorithm by adopting a shapelet transformation. However, there are two important limitations of shapelet transformation. First, the number of shapelets selected for transformation has great influence on classification result, but it is difficult to decide the quantity of shapelets which yields the best data for classification. Second, similar shapelets always exist among the selected shapelets in previous algorithms. In our work, the latter problem is addressed by introducing an efficient and effective pruning technique, it filters similar shapelets and decreases the number of candidate shapelets at the same time. Then, we propose a novel shapelet coverage method to select shapelets for a given dataset. The final selected shapelets are named after Discriminative Shapelets. Our experimental results demonstrate that, on the classic benchmark datasets used for time series classification, shapelet pruning and coverage method outperforms ShapeletFilter.
Year
DOI
Venue
2014
10.1142/S0218001414500141
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
Field
DocType
Time series classification, shapelets transformation, discriminative shapelets
Pattern recognition,Artificial intelligence,Classifier (linguistics),Discriminative model,Machine learning,Mathematics,Time series classification
Journal
Volume
Issue
ISSN
28
6
0218-0014
Citations 
PageRank 
References 
5
0.44
11
Authors
3
Name
Order
Citations
PageRank
Jidong Yuan1186.45
Zhihai Wang242528.26
Meng Han3122.25