Addressing imbalance in multilabel classification: Measures and random resampling algorithms

By Francisco Charte, Antonio Jesús Rivera, M. J. Jesús, Francisco Herrera. Neurocomputing, 2015

294 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1976d9fe7620a5e05c52621b241b4c25b89153c9

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Citations in the corpus

A Survey of Predictive Modeling on Imbalanced Domains (2016, background): “…this problem within other contexts such as regression [Torgo et al. 2013], ordinal classiﬁcation [P´erez-Ortiz et al. 2014], multi-label classiﬁcation [Charte et al. 2015b], association rules mining [Luna et al. 2015], multi-instance learning [Wang et al. 2013b], and data streams [Wang and Abraham…”
Big data preprocessing: methods and prospects (2016, methodology): “Thus, methods for re-sampling these particular data sets have been proposed [52, 53].”
Binary relevance for multi-label learning: an overview (2018, methodology): “Specifically, by decomposing the multi-label learning task into q independent binary learning tasks, each of them will be addressed by prevalent binary imbalance learning techniques such as over-/under-sampling [32, 36, 37], thresholding the decision boundary [31, 33, 34], or optimizing imbalance-sp”
QUINTA: A question tagging assistant to improve the answering ratio in electronic forums (2015, methodology): “In an IR system the vector for each document is compared with the one generated by a query, usually through a common technique known as TF/IDF (Term Frequency/Inverse Document Frequency, [8]) and a similarity metric such as the cosine distance [9].”
Towards Unsupervised Learning of Speech Features in the Wild (2021, unknown): “Finally, label imbalance can be addressed by re-weighting the loss [21] or by differently resampling the data [22].”
A Systematic Review on Imbalanced Data Challenges in Machine Learning (2019, methodology): “Hybrid sampling techniques are proposed in Qian et al. (2014) and Charte et al. (2015).”
A comprehensive data level analysis for cancer diagnosis on imbalanced data (2019, unknown): “The higher IR indicates more imbalances [70].”