现实世界的数据集严重倾斜，其中一些类的数量明显超过其他类。在这些情况下，机器学习算法在预测这些代表性不足的实例时无法实现实质性的功效。为了解决这个问题，已经提出了许多变体的合成少数过采样方法（SMOTE）来平衡处理连续特征的数据集。然而，对于同时具有标称和连续特征的数据集，SMOTE-NC是唯一基于SMOTE的过采样技术来平衡数据。本文提出了一种新的少数类过采样方法SMOTE-ENC（SMOTE - Encoded Nominal and Continuous），该方法将名词性特征编码为数值，两个数值之差反映了与少数类关联的变化量。我们的实验表明，使用SMOTE-ENC方法的分类模型比使用SMOTE-NC的模型提供更好的预测时，数据集有大量的名义功能，也有一些分类功能和目标类之间的关联。此外，我们提出的方法解决了SMOTE-NC算法的主要局限性之一。SMOTE-NC只能应用于具有由连续和名义特征组成的特征的混合数据集，并且如果数据集的所有特征都是名义特征，则无法运行。我们的新方法已被推广应用于混合数据集和名义上只有数据集。代码可从mkhushi.github.io获得
英文摘要：
Real world datasets are heavily skewed where some classes are significantly outnumbered by the other classes. In these situations, machine learning algorithms fail to achieve substantial efficacy while predicting these under-represented instances. To solve this problem, many variations of synthetic minority over-sampling methods (SMOTE) have been proposed to balance the dataset which deals with continuous features. However, for datasets with both nominal and continuous features, SMOTE-NC is the only SMOTE-based over-sampling technique to balance the data. In this paper, we present a novel minority over-sampling method, SMOTE-ENC (SMOTE - Encoded Nominal and Continuous), in which, nominal features are encoded as numeric values and the difference between two such numeric value reflects the amount of change of association with minority class. Our experiments show that the classification model using SMOTE-ENC method offers better prediction than model using SMOTE-NC when the dataset has a substantial number of nominal features and also when there is some association between the categorical features and the target class. Additionally, our proposed method addressed one of the major limitations of SMOTE-NC algorithm. SMOTE-NC can be applied only on mixed datasets that have features consisting of both continuous and nominal features and cannot function if all the features of the dataset are nominal. Our novel method has been generalized to be applied on both mixed datasets and on nominal only datasets. The code is available from mkhushi.github.io
HyperSMOTE: A Hypergraph-based Oversampling Approach for Imbalanced Node Classifications
School of Information University of Michigan, USA；College of Electronics；Information Engineering Tongji University, China；School of Computer Science；AI Wuhan University of Technology, China , z\
Large Language Models for Imbalanced Classification: Diversity makes the difference
Applied Artificial Intelligence Initiative (A 2 I 2 )（应用人工智能倡议（A2I2））；Deakin University（德肯大学）；Black Dog Institute（黑狗研究所）；University of New South Wales（新南威尔士大学）
Advancing Tabular Stroke Modelling Through a Novel Hybrid Architecture and Feature-Selection Synergy
Department of Computer Science and Engineering, Leading University, Sylhet 3112, Bangladesh（计算机科学与工程系，领先大学，锡尔het 3112，孟加拉国）；DeepNet Research and Development Lab, Sylhet 3100, Bangladesh（深网研究与发展实验室，锡尔het 3100，孟加拉国）
