Cost-sensitive learning
Cost-sensitive learning is an aspect of algorithm-level modifications for class imbalance.
Here, instead of using a standard error-driven evaluation (or 0–1 loss function), a …
Here, instead of using a standard error-driven evaluation (or 0–1 loss function), a …
Cost-sensitive convolutional neural networks for imbalanced time series classification
Y Geng, X Luo - Intelligent Data Analysis, 2019 - content.iospress.com
Time series classification and class imbalance problem are two common issues in a
multitude of real-life scenarios. This paper simultaneously explores both issues with deep …
multitude of real-life scenarios. This paper simultaneously explores both issues with deep …
A multivariate time series streaming classifier for predicting hard drive failures [application notes]
Digital data storage systems such as hard drives can suffer breakdowns that cause the loss
of stored data. Due to the cost of data and the damage that its loss entails, hard drive failure …
of stored data. Due to the cost of data and the damage that its loss entails, hard drive failure …
Time-series Shapelets with Learnable Lengths
Shapelets are subsequences that are effective for classifying time-series instances.
Learning shapelets by a continuous optimization has recently been studied to improve …
Learning shapelets by a continuous optimization has recently been studied to improve …
Density-aware personalized training for risk prediction in imbalanced medical data
Medical events of interest, such as mortality, often happen at a low rate in electronic medical
records, as most admitted patients survive. Training models with this imbalance rate (class …
records, as most admitted patients survive. Training models with this imbalance rate (class …
One-class learning time-series shapelets
A Yamaguchi, T Nishikawa - … Conference on Big Data (Big Data …, 2018 - ieeexplore.ieee.org
Shapelets are time-series segments effective for classifying time-series datasets. In recent
years, the discovery of shapelets by classifier learning has been studied. Methods for …
years, the discovery of shapelets by classifier learning has been studied. Methods for …
Regularized shapelet learning for scalable time series classification
H Zhao, Z Pan, W Tao - Computer Networks, 2020 - Elsevier
Time series shapelets are subsequences that best split time series data into classes.
Therefore, shapelet discovery has attracted considerable interest in the time series …
Therefore, shapelet discovery has attracted considerable interest in the time series …
Learning Evolvable Time-series Shapelets
Shapelets are subsequences that are effective for classifying time-series instances. In this
study, we consider when each time-series instance is obtained as progress, and formulate …
study, we consider when each time-series instance is obtained as progress, and formulate …
Bayesian forecasting with a regime-switching zero-inflated multilevel Poisson regression model: An application to adolescent alcohol use with spatial covariates
In this paper, we present and evaluate a novel Bayesian regime-switching zero-inflated
multilevel Poisson (RS-ZIMLP) regression model for forecasting alcohol use dynamics. The …
multilevel Poisson (RS-ZIMLP) regression model for forecasting alcohol use dynamics. The …
Learning time-series shapelets enhancing discriminability
Shapelets are subsequences that are effective for classifying time-series instances. Joint
learning of both classifiers and shapelets has recently been studied because this approach …
learning of both classifiers and shapelets has recently been studied because this approach …