Background Data augmentation (DA) has recently been demonstrated to achieve
considerable performance gains for deep learning (DL)—increased accuracy and stability …

The Synthetic Minority Oversampling Technique (SMOTE) preprocessing algorithm is
considered" de facto" standard in the framework of learning from imbalanced data. This is …

Sales volume forecasting is of great significance to E-commerce companies. Accurate sales
forecasting enables managers to make reasonable resource allocation in advance. In this …

Multiple-instance learning has become popular over recent years due to its use in some
special scenarios. It is basically a type of weakly supervised learning where the learning …

近年来不平衡分类问题受到广泛关注. SMOTE 过采样通过添加生成的少数类样本改变不平衡
数据集的数据分布, 是改善不平衡数据分类模型性能的流行方法之一. 本文首先阐述了SMOTE …

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 …

This paper proposes a hybrid approach based on seasonal autoregressive integrated
moving average (SARIMA) and neural networks for multi-step ahead wind speed forecasting …

In reliability analysis, lifetime data may be heavily censored and this censoring can have an
adverse effect on parameter estimates. Using maximum-likelihood estimation (MLE) to …

Time series forecasting is a challenging task, where the non-stationary characteristics of
data portray a hard setting for predictive tasks. A common issue is the imbalanced …

Situational awareness in sanitary sewer systems requires accurate flow information at
different spatial locations in a city. It is especially desirable to predict flows across a …