Year: 2019
Type: conference-paper
Abstract: In many time-sensitive applications, knowing the classification results as early as possible while preserving the accuracy is extremely important for further actions. Shapelet-based early classification methods are popular due to their natural interpretability. However, most of the existing shapelet-based methods ignore the distance information between the shapelets and the time series. The distance information, though may contain some noise, can reflect more information between the shapelets and the time series. Some existing works adopt the distance information, but are not robust to the noise in the distance information. To tackle this challenge, we present a novel distance transformation based early classification (DTEC) framework, which transfers the original time series into the distance space. Upon the distance space, a probabilistic classifier is trained, and a novel classification criterion confidence area is proposed in order to overcome the noise brought by the training phase and the dataset. The effectiveness of the proposed framework is validated on three time series benchmarks as well as the extensive datasets selected from UCR time series archive.
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
Funder: National Science Foundation
Awards: CAREER: Mining Reliable Information from Crowdsourced Data, III: Small: Dynamic Social Network Mining: Feature Extraction, Modeling and Anomaly Detection, III: Medium: High-Dimensional Interaction Analysis in Bio-Data Sets, EAGER: Medical Knowledge Graph Construction from Heterogeneous Sources
OpenAlex ID: W2943973369