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SMOTE: Synthetic Minority Over-sampling Technique

A combination of the method of oversampling the minority (abnormal) class and under-sampling the majority class can achieve better classifier performance (in ROC space) and a combination of these methods and the area under the Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy is evaluated.
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metapath2vec: Scalable Representation Learning for Heterogeneous Networks

Two scalable representation learning models, namely metapath2vec and metapATH2vec++, are developed that are able to not only outperform state-of-the-art embedding models in various heterogeneous network mining tasks, but also discern the structural and semantic correlations between diverse network objects.
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SMOTEBoost: Improving Prediction of the Minority Class in Boosting

This paper presents a novel approach for learning from imbalanced data sets, based on a combination of the SMOTE algorithm and the boosting procedure, which shows improvement in prediction performance on the minority class and overall improved F-values.
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New perspectives and methods in link prediction

This paper examines important factors for link prediction in networks and provides a general, high-performance framework for the prediction task and presents an effective flow-based predicting algorithm, formal bounds on imbalance in sparse network link prediction, and employ an evaluation method appropriate for the observed imbalance.
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Heterogeneous Graph Neural Network

HetGNN, a heterogeneous graph neural network model, is proposed that can outperform state-of-the-art baselines in various graph mining tasks, i.e., link prediction, recommendation, node classification and clustering and inductive node classification & clustering.
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Editorial: special issue on learning from imbalanced data sets

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Data Mining for Imbalanced Datasets: An Overview

  • N. Chawla
  • Computer Science
    The Data Mining and Knowledge Discovery Handbook
  • 2005
In this Chapter, some of the sampling techniques used for balancing the datasets, and the performance measures more appropriate for mining imbalanced datasets are discussed.
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A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data

This paper proposes a Multi-Scale Convolutional Recurrent Encoder-Decoder (MSCRED), to perform anomaly detection and diagnosis in multivariate time series data and demonstrates that MSCRED can outperform state-of-the-art baseline methods.
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SVMs Modeling for Highly Imbalanced Classification

Of the four SVM variations considered in this paper, the novel granular SVMs-repetitive undersampling algorithm (GSVM-RU) is the best in terms of both effectiveness and efficiency.
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A unifying view on dataset shift in classification

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