Learning from imbalanced data: open challenges and future directions

By Bartosz Krawczyk. 2016

2,330 citations recorded by the source corpus.

Despite more than two decades of continuous development learning from imbalanced data is still a focus of intense research. Starting as a problem of skewed distributions of binary tasks, this topic evolved way beyond this conception. With the expansion of machine learning and data mining, combined with the arrival of big data era, we have gained a deeper insight into the nature of imbalanced learning, while at the same time facing new emerging challenges. Data-level and algorithm-level methods are constantly being improved and hybrid approaches gain increasing popularity. Recent trends focus on analyzing not only the disproportion between classes, but also other difficulties embedded in the nature of data. New real-life problems motivate researchers to focus on computationally efficient, adaptive and real-time methods. This paper aims at discussing open issues and challenges that need to be addressed to further develop the field of imbalanced learning. Seven vital areas of research in this topic are identified, covering the full spectrum of learning from imbalanced data: classification, regression, clustering, data streams, big data analytics and applications, e.g., in social media and computer vision. This paper provides a discussion and suggestions concerning lines of future research for each of them.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

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Citations in the corpus

Imbalanced Continual Learning with Partitioning Reservoir Sampling (2020, background): “Imbalanced data induce severe issues in learning that are primarily attributed to gradient dominance and under-representation of the minority [44,21,98,80].”
Ensemble learning for data stream analysis: A survey (2017, background): “It has been more studied in this static framework and many new algorithms have already been introduced, for their comprehensive review see the recent monograph [74] or surveys [9, 73, 103].”
Convolutional Neural Networks for the Automatic Identification of Plant Diseases (2019, background): “Indeed, acquiring more than 1000 samples per class, in several plots in Spain and Germany, over three seasons (2014, 2015, and 2016) and in real environments resulted in enough diversity for the model to work well in operational conditions.”
MFC-GAN: Class-imbalanced dataset classification using Multiple Fake Class Generative Adversarial Network (2019, methodology): “Common methods such as Synthetic Minority Over-Sampling Technique (SMOTE) proved to be ineffective in handling class-imbalance in extreme cases (hugely imbalanced datasets) and results in performance deterioration of the learning algorithm in such scenarios [2].”
A comprehensive data level analysis for cancer diagnosis on imbalanced data (2019, background): “As such, the rate of correct diagnosis should be improved for the learning algorithms [20].”
"Let Me Tell You About Your Mental Health!": Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention (2018, background): “Ensemble learning algorithms hybridized by sampling methods have proved to outperform in the cases of imbalanced data[19].”
Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing (2024, background): “Users typically control the level of uncertainty by choosing different decoding strategies such as nucleus sampling (Holtzman et al., 2019), as well as the values of the hyper-parameter temperature .”
A Comparative Performance Analysis of Data Resampling Methods on Imbalance Medical Data (2021, background): “This is because, in most applications, the minority class is often more important, requiring methods with improved recognition rates [75], [76], and errors (FN and FP) have varying degrees of consequence.”
A survey on data preprocessing for data stream mining: Current status and future directions (2017, background): “Such techniques are crucial for imbalanced [138, 139], multi-label [140] and multi-instance [141] problems and should be extended into the streaming framework.”
DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data (2021, background): “LEARNING from imbalanced data is among the most crucial problems faced by the machine learning community [1].”