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1. Examining Effects of Class Imbalance on Conditional GAN Training

   https://doi.org/10.1007/978-3-031-42505-9_40  

   Chen Y.; Kempton D. J.; Angryk R. A. (September 2023, Proceedings of the 22nd International Conference on Artificial Intelligence and Soft Computing, (ICAISC).)

   Rutkowski L.; Scherer R.; Korytkowski M.; Pedrycz W.; Tadeusiewicz R.; Zurada J. (Ed.)

   In this work, we investigate the impact of class imbalance on the accuracy and diversity of synthetic samples generated by conditional generative adversarial networks (CGAN) models. Though many studies utilizing GANs have seen extraordinary success in producing realistic image samples, these studies generally assume the use of well-processed and balanced benchmark image datasets, including MNIST and CIFAR-10. However, well-balanced data is uncommon in real world applications such as detecting fraud, diagnosing diabetes, and predicting solar flares. It is well known that when class labels are not distributed uniformly, the predictive ability of classification algorithms suffers significantly, a phenomenon known as the "class-imbalance problem." We show that the imbalance in the training set can also impact sample generation of CGAN models. We utilize the well known MNIST datasets, controlling the imbalance ratio of certain classes within the data through sampling. We are able to show that both the quality and diversity of generated samples suffer in the presence of class imbalances and propose a novel framework named Two-stage CGAN to produce high-quality synthetic samples in such cases. Our results indicate that the proposed framework provides a significant improvement over typical oversampling and undersampling techniques utilized for class imbalance remediation. 

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2. How to Train Your Flare Prediction Model: Revisiting Robust Sampling of Rare Events

   https://doi.org/10.3847/1538-4365/abec88  

   Ahmadzadeh, Azim; Aydin, Berkay; Georgoulis, Manolis K.; Kempton, Dustin J.; Mahajan, Sushant S.; Angryk, Rafal A. (May 2021, The Astrophysical Journal Supplement Series)

   Abstract We present a case study of solar flare forecasting by means of metadata feature time series, by treating it as a prominent class-imbalance and temporally coherent problem. Taking full advantage of pre-flare time series in solar active regions is made possible via the Space Weather Analytics for Solar Flares (SWAN-SF) benchmark data set, a partitioned collection of multivariate time series of active region properties comprising 4075 regions and spanning over 9 yr of the Solar Dynamics Observatory period of operations. We showcase the general concept of temporal coherence triggered by the demand of continuity in time series forecasting and show that lack of proper understanding of this effect may spuriously enhance models’ performance. We further address another well-known challenge in rare-event prediction, namely, the class-imbalance issue. The SWAN-SF is an appropriate data set for this, with a 60:1 imbalance ratio for GOES M- and X-class flares and an 800:1 imbalance ratio for X-class flares against flare-quiet instances. We revisit the main remedies for these challenges and present several experiments to illustrate the exact impact that each of these remedies may have on performance. Moreover, we acknowledge that some basic data manipulation tasks such as data normalization and cross validation may also impact the performance; we discuss these problems as well. In this framework we also review the primary advantages and disadvantages of using true skill statistic and Heidke skill score, two widely used performance verification metrics for the flare-forecasting task. In conclusion, we show and advocate for the benefits of time series versus point-in-time forecasting, provided that the above challenges are measurably and quantitatively addressed. 

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3. Interpretable sensitivity analysis for balancing weights

   https://doi.org/10.1093/jrsssa/qnad032  

   Soriano, Dan; Ben-Michael, Eli; Bickel, Peter J; Feller, Avi; Pimentel, Samuel D (March 2023, Journal of the Royal Statistical Society Series A: Statistics in Society)

   Abstract Assessing sensitivity to unmeasured confounding is an important step in observational studies, which typically estimate effects under the assumption that all confounders are measured. In this paper, we develop a sensitivity analysis framework for balancing weights estimators, an increasingly popular approach that solves an optimization problem to obtain weights that directly minimizes covariate imbalance. In particular, we adapt a sensitivity analysis framework using the percentile bootstrap for a broad class of balancing weights estimators. We prove that the percentile bootstrap procedure can, with only minor modifications, yield valid confidence intervals for causal effects under restrictions on the level of unmeasured confounding. We also propose an amplification—a mapping from a one-dimensional sensitivity analysis to a higher dimensional sensitivity analysis—to allow for interpretable sensitivity parameters in the balancing weights framework. We illustrate our method through extensive real data examples. 

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4. Clustering-Augmented Fraud Detection on Graphs Using Label-Aware Feature Aggregation

   Jing, Shixiong; Chen, Lingwei; Wu, Dinghao (December 2024, The 16th Asian Conference on Machine Learning (Conference Track))

   Fraud detection has emerged as a pivotal process in different fields (e.g., e-commerce, social networks). Since interactions among entities provide valuable insights into fraudulent activities, such behaviors can be naturally represented as graphs, where graph neural networks (GNNs) have been developed as prominent models to boost the efficacy of fraud detection. However, the application of GNNs in this domain encounters significant challenges, primarily due to class imbalance and a mixture of homophily and heterophily of fraud graphs. To address these challenges, in this paper, we propose LACA, which implements fraud detection on graphs using Label-Aware feature aggregation to advance GNN training, which is regularized by Clustering Augmented optimization. Specifically, label-aware feature aggregation simplifies adaptive aggregation in homophily-heterophily mixed neighborhoods, preventing gradient domination by legitimate nodes and mitigating class imbalance in message passing. Clustering-augmented optimization provides fine-grained subclass semantics to improve detection performance, and yields additional benefit in addressing class imbalance. Extensive experiments on four fraud datasets demonstrate that LACA can significantly improve fraud detection performance on graphs with different imbalance ratios and homophily ratios, outperforming state-of-the-art GNN models. 

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5. Prediction of Bacterial sRNAs Using Sequence-Derived Features and Machine Learning

   https://doi.org/10.1177/11779322221118335  

   Jha, Tony; Mendel, Jovinna; Cho, Hyuk; Choudhary, Madhusudan (January 2022, Bioinformatics and Biology Insights)

   Small ribonucleic acid (sRNA) sequences are 50–500 nucleotide long, noncoding RNA (ncRNA) sequences that play an important role in regulating transcription and translation within a bacterial cell. As such, identifying sRNA sequences within an organism’s genome is essential to understand the impact of the RNA molecules on cellular processes. Recently, numerous machine learning models have been applied to predict sRNAs within bacterial genomes. In this study, we considered the sRNA prediction as an imbalanced binary classification problem to distinguish minor positive sRNAs from major negative ones within imbalanced data and then performed a comparative study with six learning algorithms and seven assessment metrics. First, we collected numerical feature groups extracted from known sRNAs previously identified in Salmonella typhimurium LT2 (SLT2) and Escherichia coli K12 ( E. coli K12) genomes. Second, as a preliminary study, we characterized the sRNA-size distribution with the conformity test for Benford’s law. Third, we applied six traditional classification algorithms to sRNA features and assessed classification performance with seven metrics, varying positive-to-negative instance ratios, and utilizing stratified 10-fold cross-validation. We revisited important individual features and feature groups and found that classification with combined features perform better than with either an individual feature or a single feature group in terms of Area Under Precision-Recall curve (AUPR). We reconfirmed that AUPR properly measures classification performance on imbalanced data with varying imbalance ratios, which is consistent with previous studies on classification metrics for imbalanced data. Overall, eXtreme Gradient Boosting (XGBoost), even without exploiting optimal hyperparameter values, performed better than the other five algorithms with specific optimal parameter settings. As a future work, we plan to extend XGBoost further to a large amount of published sRNAs in bacterial genomes and compare its classification performance with recent machine learning models’ performance. 

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