The long-standing challenge of building effective classification models for small and imbalanced datasets has seen little improvement since the creation of the Synthetic Minority Over-sampling Technique (SMOTE) over 20 years ago. Though GAN based models seem promising, there has been a lack of purpose built architectures for solving the aforementioned problem, as most previous studies focus on applying already existing models. This paper proposes a unique, performance-oriented, data-generating strategy that utilizes a new architecture, coined draGAN, to generate both minority and majority samples. The samples are generated with the objective of optimizing the classification model's performance, rather than similarity to the real data. We benchmark our approach against state-of-the-art methods from the SMOTE family and competitive GAN based approaches on 94 tabular datasets with varying degrees of imbalance and linearity. Empirically we show the superiority of draGAN, but also highlight some of its shortcomings. All code is available on: https://github.com/LeonGuertler/draGAN.
Amasya University（阿马西亚大学）；Bilkent University（比尔肯特大学）；National Magnetic Resonance Research Center, Bilkent University（比尔肯特大学国家磁共振研究中心）；Sabuncu Brain Research Center, Bilkent University（比尔肯特大学萨本朱脑研究中心）
Melanoma is a fatal skin cancer that is curable and has dramatically increasing survival rate when diagnosed at early stages. Learning-based methods hold significant promise for the detection of melanoma from dermoscopic images. However, since melanoma is a rare disease, existing databases of skin lesions predominantly contain highly imbalanced numbers of benign versus malignant samples. In turn, this imbalance introduces substantial bias in classification models due to the statistical dominance of the majority class. To address this issue, we introduce a deep clustering approach based on the …
Active learning aims to optimize the dataset annotation process when resources are constrained. Most existing methods are designed for balanced datasets. Their practical applicability is limited by the fact that a majority of real-life datasets are actually imbalanced. Here, we introduce a new active learning method which is designed for imbalanced datasets. It favors samples likely to be in minority classes so as to reduce the imbalance of the labeled subset and create a better representation for these classes. We also compare two training schemes for active learning: (1) the one commonly dep…
Evaluation of AutoML Frameworks for IDS under Imbalanced Data Conditions of the NSL-KDD Dataset
Cybersecurity and Artificial Intelligence Laboratory (CS&I Lab), National Institute of Telecommunications (Inatel)（网络安全与人工智能实验室（CS&I Lab），国家电信研究所（Inatel））；Wireless and Artificial Intelligence Laboratory (WAI Lab), National Institute of Telecommunications (Inatel)（无线与人工智能实验室（WAI Lab），国家电信研究所（Inatel））
The capability of the traditional semi-supervised learning (SSL) methods is far from real-world application due to severely biased pseudo-labels caused by (1) class imbalance and (2) class distribution mismatch between labeled and unlabeled data. This paper addresses such a relatively under-explored problem. First, we propose a general pseudo-labeling framework that class-adaptively blends the semantic pseudo-label from a similarity-based classifier to the linear one from the linear classifier, after making the observation that both types of pseudo-labels have complementary properties in terms…
Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring
Risk Management Division, China Development Bank（国家开发银行风险管理部）；School of Finance, Central University of Finance and Economics（中央财经大学金融学院）；GF Holdings (Hong Kong) Corporation Limited（广发控股（香港）有限公司）；University of Cambridge（剑桥大学）
School of Microelectronics and Communication Engineering, Chongqing University（重庆大学微电子与通信工程学院）
The challenge of imbalanced learning lies not only in class imbalance problem, but also in the class overlapping problem which is complex. However, most of the existing algorithms mainly focus on the former. The limitation prevents the existing methods from breaking through. To address this limitation, this paper proposes an ensemble learning algorithm based on dual clustering and stage-wise hybrid sampling (DCSHS). The DCSHS has three parts. Firstly, we design a projection clustering combination framework (PCC) guided by Davies-Bouldin clustering effectiveness index (DBI), which is used to ob…
A Unified Perspective for Loss-Oriented Imbalanced Learning via Localization
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（人工智能安全国家重点实验室，计算技术研究所，中国科学院）；Peng Cheng Laboratory（鹏城实验室）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；Artificial Intelligence Institute of China Electronics Technology Group Corporation（中国电子科技集团有限公司人工智能研究院）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络科学与技术学院）；Key Laboratory of Big Data Mining and Knowledge Management (BDKM), University of Chinese Academy of Sciences（大数据挖掘与知识管理关键实验室（BDKM），中国科学院大学）
Adaptive kernel-density approach for imbalanced binary classification
Graduate School of Science and Technology, Nara Institute of Science and Technology, Japan（科学技术研究生院，科学与技术国立研究所，日本）；Data Science Center, Nara Institute of Science and Technology, Japan（数据科学中心，科学与技术国立研究所，日本）
Enhancing stop location detection for incomplete urban mobility datasets
IMT School for Advanced Studies Lucca（卢卡IMT高等研究院）；Carlos III University of Madrid（马德里卡洛斯三世大学）；Caja Blanca Datos SL（Caja Blanca Datos SL公司）；University of Amsterdam（阿姆斯特丹大学）；University of Zaragoza（萨拉戈萨大学）；University of Padua（帕多瓦大学）；Cuebiq Inc.（Cuebiq公司）
Skew-Probabilistic Neural Networks for Learning from Imbalanced Data
Khalifa University of Science and Technology（卡利法科学与技术大学）；Sorbonne University Abu Dhabi（索邦大学阿布扎比分校）；Duke-NUS Medical School, National University of Singapore（新加坡国立大学杜克-奈瑟医学学校）；Department of Statistics and Data Science, National University of Singapore（新加坡国立大学统计与数据科学系）；Department of Biostatistics and Bioinformatics, Duke University（杜克大学生物统计学与生物信息学系）
Beihang University（北京航空航天大学）；Science & Technology on Reliability & Environmental Engineering Laboratory（可靠性与环境工程技术重点实验室）
Cross-project defect prediction (CPDP) aims to predict defects of projects lacking training data by using prediction models trained on historical defect data from other projects. However, since the distribution differences between datasets from different projects, it is still a challenge to build high-quality CPDP models. Unfortunately, class imbalanced nature of software defect datasets further increases the difficulty. In this paper, we propose a transferlearning oriented minority over-sampling technique (TOMO) based feature weighting transfer naive Bayes (FWTNB) approach (TOMOFWTNB) for CPD…
Imbalanced Classification under Capacity Constraints
Departamento de Matemática y Ciencias Universidad de San Andrés（数学与科学系，圣安德烈斯大学）；CONICET Argentina（阿根廷国家科研委员会）；PEDECIBA Matemática Uruguay（乌拉圭PEDECIBA数学）
