Minimum Recall-Based Loss Function for Imbalanced Time Series Classification
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Minimum Recall-Based Loss Function for Imbalanced Time Series Classification
Authors
Keywords
-
Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 35, Issue 10, Pages 10024-10034
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Online
2023-04-21
DOI
10.1109/tkde.2023.3268994
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- A Multivariate Time Series Streaming Classifier for Predicting Hard Drive Failures [Application Notes]
- (2022) Josu Ircio et al. IEEE Computational Intelligence Magazine
- ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels
- (2020) Angus Dempster et al. DATA MINING AND KNOWLEDGE DISCOVERY
- InceptionTime: Finding AlexNet for time series classification
- (2020) Hassan Ismail Fawaz et al. DATA MINING AND KNOWLEDGE DISCOVERY
- MSCS-DeepLN: Evaluating lung nodule malignancy using multi-scale cost-sensitive neural networks
- (2020) Xiuyuan Xu et al. MEDICAL IMAGE ANALYSIS
- Imbalanced Classification Based on Minority Clustering Synthetic Minority Oversampling Technique With Wind Turbine Fault Detection Application
- (2020) Huaikuan Yi et al. IEEE Transactions on Industrial Informatics
- AUC-Based Extreme Learning Machines for Supervised and Semi-Supervised Imbalanced Classification
- (2020) Guanjin Wang et al. IEEE Transactions on Systems Man Cybernetics-Systems
- Deep learning for time series classification: a review
- (2019) Hassan Ismail Fawaz et al. DATA MINING AND KNOWLEDGE DISCOVERY
- Optimizing shapelets quality measure for imbalanced time series classification
- (2019) Qiuyan Yan et al. APPLIED INTELLIGENCE
- Multivariate LSTM-FCNs for time series classification
- (2019) Fazle Karim et al. NEURAL NETWORKS
- A Survey of Optimization Methods From a Machine Learning Perspective
- (2019) Shiliang Sun et al. IEEE Transactions on Cybernetics
- Imbalance: Oversampling algorithms for imbalanced classification in R
- (2018) Ignacio Cordón et al. KNOWLEDGE-BASED SYSTEMS
- Measuring the class-imbalance extent of multi-class problems
- (2017) Jonathan Ortigosa-Hernández et al. PATTERN RECOGNITION LETTERS
- A Parsimonious Mixture of Gaussian Trees Model for Oversampling in Imbalanced and Multimodal Time-Series Classification
- (2014) Hong Cao et al. IEEE Transactions on Neural Networks and Learning Systems
- Integrated Oversampling for Imbalanced Time Series Classification
- (2013) Hong Cao et al. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
- Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power
- (2009) Salvador García et al. INFORMATION SCIENCES
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started