把前段時間看的文獻整理了一下，主要是關於利用深度學習，時間序列，不平衡分類的相關研究工作
Paper list of Imbalanced Time-series Classification with Deep Learning
Deep Learning for Imbalanced Time-Series Classification
- Towards Effective Classification of Imbalanced Data with Convolutional Neural Networks
- Cost-sensitive Deep Learning for Early Readmission Prediction at A Major Hospital
Deep Learning for Imbalance Classificaiton
Survey
Cost-Sensitive
- Cost-Sensitive Learning with Neural Networks
- Training Cost-sensitive Deep Belief Networks on Imbalance Data Problems
- Cost-Sensitive Deep Learning with Layer-Wise Cost Estimation
- Training Cost-Sensitive Neural Networks with Methods Addressing the Class Imbalanced Problem
- A Weight-Selection Strategy on Training Deep Neural Networks for Imbalanced Classification
- Cost-Sensitive Learning of Deep Feature Representations from Imbalanced Data
- Cost-aware Pre-training for Multiclass Cost-sensitive Deep Learning
- Cost-Sensitive Learning of Deep Feature Representations from Imbalanced Data
Sampling
- Effective data generation for imbalance learning using conditional generative adversarial networks
- Diversified Sensitivity-Based Undersampling for Imbalance Classification Problems
- Learning Deep Representation for Imbalanced Classification
- Deep Learning for Imbalanced Multimedia Data Classification
Others
- CNUSVM: Hybrid CNN-Uneven SVM Model for Imbalanced Visual Learning
- Training Deep Neural Networks on Imbalanced Data Sets
- Deep Learning with MCA-based Instance Selection and Boostrapping for Imbalanced Data Classification
Deep Learning for Time-Series Classification
Time-Series Classification
- Predicting Defective Engines using Convolutional Neural Networks on Temporal Vibration Signals
- Earliness-Aware Deep Convolutional Networks for Early Time Series Classification
- Data Augmentation for Time Series Classification using Convolutional Neural Networks
- Multi-Scale Convolutional Neural Networks for Time Series
- Deep Learning for Time-Series Analysis
- LSTM Fully Convolutional Networks for Time Series Classification
- Time series classification using Multi-channels deep convolutinal neural networks
- Convolutional Nonlinear Neighbourhood Components Analysis for Time Series Classification
- Exploiting Multi-Channels Deep Convolutional Neural Networks for Multivariate Time Series Classification
- Encoding Time Series as Images for Visual Inspection and Classification Using Tiled Convolutional Neural Networks
- Spatially Encoding Temporal Correlations to Classify Temporal Data Using Convolutional Neural Networks
Application
- A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time series
- Convolutional Neural Network for Time Series Cattle Behaviour Classification
- A deep convolutional neural network model to classify heatbeats
- Learning Robust Features using Deep Learning for Automatic Seizure Detection
Time-Series Transfer to Images
- Encoding Time Series as Images for Visual Inspection and Classification Using Tiled Convolutional Neural Networks
- Spatially Encoding Temporal Correlations to Classify Temporal Data Using Convolutional Neural Networks
- Encoding Physiological Signals as Images for Affective State Recognition Using Convolutional Neural Networks
- Representation Learning Deconvolutional for Multivariate Time Seres Classification
Imbalanced Time-Series Classification
Sampling
- Model-Based Oversampling for Imbalanced Sequence Classification
- Integrated Oversampling for Imbalanced Time Series Classification
- An Effective Method for Imbalanced Time Series Classification: Hybrid Sampling
- SPO: Structure Preserving Oversampling for Imbalanced Time Series Classification
- A Comparative Study of Sampling Methods and Algorithms for Imbalanced Time Series Classification
- Improving SVM classification on imbalanced time series data sets with ghost points
- A Parsimonious Mixture of Gaussian Trees Model for Oversampling in Imbalanced and Multimodal Time-Series Classification
Cost-Sensitive
Others
Time-Series Classification
- The Great Time Series Classification Bake Off An Experimental Evaluation of Recently Proposed Algorithms. Extended Version
- Support vector-based algorithms with weight dynamic time warping kernel function for time series classification
- Early classification on multivariate time series
- Imaging Time-Series to Improve Classification and Imputation
Imbalance CLassification
Survey
- An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics
- Learning from class-imbalanced data: review of methods and applications
- Learning from imbalanced data
Sampling
- A novel algorithm for imbalance data classification based on genetic algorithm improved SMOTE
- A novel svm modeling approach for highly imbalanced and overlapping classification
- Evolutionary data analysis for the class imbalance problem
- Resampling-based ensemble methods for online class imbalance Learning
- Adaptive ensemble undersampling-boosting: a novel learning framework for imbalanced data
- Fault detection for the class imbalance problem in semiconductor manufacturing processes
Cost-sensitive
- Instance categorization by support vector machines to adjust weights in adaboost for imbalanced data classification
- Optimizing Cost-sensitive svm for imbalanced data: Connecting cluster to classification
Thresholding
- Reviving threshold-moving: a simple plug-in bagging ensemble for binary and multiclass imbalanced data
- Dealing with class imbalance using thresholding