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- research-articleOctober 2023
Hierarchical Category-Enhanced Prototype Learning for Imbalanced Temporal Recommendation
MM '23: Proceedings of the 31st ACM International Conference on MultimediaPages 6181–6189https://doi.org/10.1145/3581783.3613829Temporal recommendation systems aim to suggest items to users at the optimal time. However, the significant imbalance of items in the training data poses a major challenge to predictive accuracy. Existing approaches attempt to alleviate this issue by ...
- research-articleOctober 2023
Orthogonal Uncertainty Representation of Data Manifold for Robust Long-Tailed Learning
MM '23: Proceedings of the 31st ACM International Conference on MultimediaPages 4848–4857https://doi.org/10.1145/3581783.3611698In scenarios with long-tailed distributions, the model's ability to identify tail classes is limited due to the under-representation of tail samples. Class rebalancing, information augmentation, and other techniques have been proposed to facilitate ...
- research-articleJune 2023
Semi-Supervised Hybrid Predictive Bi-Clustering Trees for Drug-Target Interaction Prediction
SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied ComputingPages 1163–1170https://doi.org/10.1145/3555776.3578606Information about interactions between objects can be used to solve many important problems. One of these important problems is drug-target interaction prediction, where different machine learning methods can be applied to solve the prediction task. ...
- research-articleJune 2023
An active learning budget-based oversampling approach for partially labeled multi-class imbalanced data streams
SAC '23: Proceedings of the 38th ACM/SIGAPP Symposium on Applied ComputingPages 382–389https://doi.org/10.1145/3555776.3577624Learning classification models from multi-class imbalanced data streams is a challenging task in machine learning. Moreover, there is a common assumption that all instances are labeled and available for the training phase. However, this is not realistic ...
- short-paperJanuary 2023
RE-RentFraud: A System to detect Frauds in rent payments for Real-Estate properties
CODS-COMAD '23: Proceedings of the 6th Joint International Conference on Data Science & Management of Data (10th ACM IKDD CODS and 28th COMAD)Pages 253–257https://doi.org/10.1145/3570991.3571066Housing.com have launched Pay Rent feature which allows tenants to pay rents to their landlords using credit cards. Over time, with increase in number of transactions there is an increase in number of payment frauds occurring on the platform. Therefore,...
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- research-articleJanuary 2023
Oversampling method based on GAN for tabular binary classification problems
Intelligent Data Analysis (INDA), Volume 27, Issue 5Pages 1287–1308https://doi.org/10.3233/IDA-220383Data-imbalanced problems are present in many applications. A big gap in the number of samples in different classes induces classifiers to skew to the majority class and thus diminish the performance of learning and quality of obtained results. ...
- research-articleOctober 2022
Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning
CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge ManagementPages 2476–2485https://doi.org/10.1145/3511808.3557474Imbalanced learning is a fundamental challenge in data mining, where there is a disproportionate ratio of training samples in each class. Over-sampling is an effective technique to tackle imbalanced learning through generating synthetic samples for the ...
- research-articleOctober 2022
CACOLIT: Cross-domain Adaptive Co-learning for Imbalanced Image-to-Image Translation
MM '22: Proceedings of the 30th ACM International Conference on MultimediaPages 1068–1076https://doi.org/10.1145/3503161.3547789State-of-the-art unsupervised image-to-image translation (I2I) methods have made great progress on transferring images from a source domain X to a target domain Y. However, training these unsupervised I2I models on imbalanced target domain (e.g., Y with ...
- research-articleAugust 2022
Pairwise Adversarial Training for Unsupervised Class-imbalanced Domain Adaptation
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data MiningPages 1598–1606https://doi.org/10.1145/3534678.3539243Unsupervised domain adaptation (UDA) has become an appealing approach for knowledge transfer from a labeled source domain to an unlabeled target domain. However, when the classes in source and target domains are imbalanced, most existing UDA methods ...
- research-articleAugust 2022
Handling class imbalance problem in software maintainability prediction: an empirical investigation
Frontiers of Computer Science: Selected Publications from Chinese Universities (FCS), Volume 16, Issue 4https://doi.org/10.1007/s11704-021-0127-0AbstractAs the complexity of software systems is increasing; software maintenance is becoming a challenge for software practitioners. The prediction of classes that require high maintainability effort is of utmost necessity to develop cost-effective and ...
- research-articleMay 2022
Observation points classifier ensemble for high‐dimensional imbalanced classification
CAAI Transactions on Intelligence Technology (CIT2), Volume 8, Issue 2Pages 500–517https://doi.org/10.1049/cit2.12100AbstractIn this paper, an Observation Points Classifier Ensemble (OPCE) algorithm is proposed to deal with High‐Dimensional Imbalanced Classification (HDIC) problems based on data processed using the Multi‐Dimensional Scaling (MDS) feature extraction ...
- research-articleJanuary 2022
The Impact of Churn Labelling Rules on Churn Prediction in Telecommunications
One of the biggest difficulties in telecommunication industry is to retain the customers and prevent the churn. In this article, we overview the most recent researches related to churn detection for telecommunication companies. The selected machine ...
- research-articleNovember 2021
CityOutlook: Early Crowd Dynamics Forecast towards Irregular Events Detection with Synthetically Unbiased Regression
SIGSPATIAL '21: Proceedings of the 29th International Conference on Advances in Geographic Information SystemsPages 207–210https://doi.org/10.1145/3474717.3483945Early crowd dynamics forecasting, such as one week in advance, plays an important role in risk-aware decision-making in urban regions such as congestion mitigation or crowd control for public safety. Although previous approaches have addressed crowd ...
- short-paperOctober 2021
Counterfactual Generative Smoothing for Imbalanced Natural Language Classification
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge ManagementPages 3058–3062https://doi.org/10.1145/3459637.3482077Classification datasets are often biased in observations, leaving onlya few observations for minority classes. Our key contribution is de-tecting and reducing Under-represented (U-) and Over-represented(O-) artifacts from dataset imbalance, by proposing ...
- research-articleOctober 2021
Disentangle Your Dense Object Detector
MM '21: Proceedings of the 29th ACM International Conference on MultimediaPages 4939–4948https://doi.org/10.1145/3474085.3475351Deep learning-based dense object detectors have achieved great success in the past few years and have been applied to numerous multimedia applications such as video understanding. However, the current training pipeline for dense detectors is compromised ...
- research-articleJuly 2021
Discovering API Directives from API Specifications with Text Classification
Journal of Computer Science and Technology (JCST), Volume 36, Issue 4Pages 922–943https://doi.org/10.1007/s11390-021-0235-1AbstractApplication programming interface (API) libraries are extensively used by developers. To correctly program with APIs and avoid bugs, developers shall pay attention to API directives, which illustrate the constraints of APIs. Unfortunately, API ...
- research-articleJuly 2021
Software defect prediction with imbalanced distribution by radius‐synthetic minority over‐sampling technique
Journal of Software: Evolution and Process (WSMR), Volume 33, Issue 7https://doi.org/10.1002/smr.2362AbstractSoftware defect prediction, which can identify the defect‐prone modules, is an effective technology to ensure the quality of software products. Due to the importance in software maintenance, many learning‐based software defect prediction models ...
- research-articleApril 2021
Just-in-time defect prediction for Android apps via imbalanced deep learning model
SAC '21: Proceedings of the 36th Annual ACM Symposium on Applied ComputingPages 1447–1454https://doi.org/10.1145/3412841.3442019Android mobile apps have played important roles in our daily life and work. To meet new requirements from users, the mobile apps encounter frequent updates, which involves in a large quantity of code commits. Previous studies proposed to apply Just-in-...
- research-articleJanuary 2021
A novel multi-stage ensemble model with multiple K-means-based selective undersampling: An application in credit scoring
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology (JIFS), Volume 40, Issue 5Pages 9471–9484https://doi.org/10.3233/JIFS-201954With the advancement of machine learning, credit scoring can be performed better. As one of the widely recognized machine learning methods, ensemble learning has demonstrated significant improvements in the predictive accuracy over individual machine ...
- research-articleJanuary 2021
Study of Multi-Class Classification Algorithms’ Performance on Highly Imbalanced Network Intrusion Datasets
This paper is devoted to the problem of class imbalance in machine learning, focusing on the intrusion detection of rare classes in computer networks. The problem of class imbalance occurs when one class heavily outnumbers examples from the other classes. ...