![]() ![]() The study also discusses important results reported so far in the literature and highlights some of their strengths and limitations to guide future research. As the first systematic study of approaches addressing an imbalanced problem in MLC, this paper provides a comprehensive survey of the state-of-the-art methods for imbalanced MLC, including the characteristics of imbalanced multi-label datasets, evaluation measures and comparative analysis of the proposed methods. In this paper, we provide a review of the approaches for handling the imbalance problem in multi-label data by collecting the existing research work. The imbalanced problem in MLC imposes challenges to multi-label data analytics which can be viewed from three perspectives: imbalance within labels, among labels, and label-sets. Book Dec 2021 Leszek Borzemski Henry SELVARAJ University of Nevada, Las Vegas, USA Jerzy wiatek This book features high-quality, peer-reviewed papers from the 28th International Conference. However, the class imbalance problem has become an inherent characteristic of many multi-label datasets, where the samples and their corresponding labels are non-uniformly distributed over the data space. MLC has gained much importance in recent years due to its wide range of application domains. Multi-Label Classification (MLC) is an extension of the standard single-label classification where each data instance is associated with several labels simultaneously. ![]()
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