Strategies for learning in class imbalance problems

By Ricardo Barandela, José Salvador Sánchez, Vicente García, E. Rangel. Pattern Recognition, 2003

605 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8e71264b0ed4937fd8e7c934e81738683db689ab

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In Artificial Intelligence

Citations in the corpus

Imbalanced Deep Learning by Minority Class Incremental Rectification (2018, methodology): “(2) Algorithmlevel: Modifying existing algorithms to give more emphasis on the minority classes [8,9,10,43,44,45,46,47,48,49,50].”
A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning (2023, positive): “We computed the geometric mean (G-mean) (Barandela et al., 2003), defined in Eq.”
SMOTE-IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering (2015, background): “These approaches can be mainly categorized in two groups [3]:”
EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling (2013, methodology): “The performance is estimated by hold-one-out technique using 1NN classi ﬁ er, and is measured by the geometric mean (GM) [3] (Eq.”
A Supervised Learning Approach to Monaural Segregation of Reverberant Speech (2009, background): “A common practice to increase system robustness against uncertain priors is training the classifier over a data set with the least biased priors [5], [34].”
A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets (2008, methodology): “The metric used in this work is the geometric mean of the true rates [1], which can be defined as GM = √acc+ · acc−, (3) where acc+ means the accuracy in the positive examples (TPrate) and acc− is the accuracy in the negative examples (TN rate).”
An insight into classification with imbalanced data: Empirical results and current trends on using data intrinsic characteristics (2013, methodology): “These approaches can be categorized into two groups: the internal approaches that create new algorithms or modify existing ones to take the class-imbalance problem into consideration [7,41,82,129,152] and external approaches that preprocess the data in order to diminish the effect of their class imb”
A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability (2009, neutral): “Well-known accuracy measures for binary-class problems are: classification rate, precision, sensitivity, specificity, G-mean (Barandela et al. 2003), F-score, AUC (Huang and Ling 2005), Youden's index c (Youden 1950) and Cohen's Kappa (Ben-David 2007).”
A novel ensemble method for classifying imbalanced data (2015, methodology): “One of the most popular methods for solving the class imbalance problems is sampling [20, 21, 22, 23].”
Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches (2013, background): “Algorithmic level solutions: These solutions try to adapt speciﬁc classiﬁcation algorithms to reinforce the learning towards the positive class.”
Web usage mining for predicting final marks of students that use Moodle courses (2013, methodology): “One of the most frequent methods used to learn from imbalanced data consists of re-sampling the data, either by over-sampling the minority classes or under-sampling the majority ones, until every class is equally represented [84].”