Random Subspace Ensembles of Fully Convolutional Network for Time Series Classification
Time series classification (TSC) task is one of the most significant topics in data mining. Among all methods for this issue, the deep-learning-based shows superior performance for its good adaption to raw series data and automatic extraction of features. However, rare eyes are kept on composing ens...
Main Authors: | Yangqianhui Zhang, Chunyang Mo, Jiajun Ma, Liang Zhao |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-11-01
|
Series: | Applied Sciences |
Subjects: | |
Online Access: | https://www.mdpi.com/2076-3417/11/22/10957 |
Similar Items
-
Comparison of Random Subspace and Voting Ensemble Machine Learning Methods for Face Recognition
by: Mehmet Akif Yaman, et al.
Published: (2018-11-01) -
Fingerprint Verification System Based on DWT, Multiple Domain Feature Extraction, and Ensemble Subspace Classifier
by: Andrés Rojas, et al.
Published: (2022-12-01) -
Random Subspace Ensemble Learning for Functional Near-Infrared Spectroscopy Brain-Computer Interfaces
by: Jaeyoung Shin
Published: (2020-07-01) -
An Ensemble Outlier Detection Method Based on Information Entropy-Weighted Subspaces for High-Dimensional Data
by: Zihao Li, et al.
Published: (2023-08-01) -
Fully Convolutional Spectral–Spatial Fusion Network Integrating Supervised Contrastive Learning for Hyperspectral Image Classification
by: Yifan Shen, et al.
Published: (2023-01-01)