Editorial: special issue on learning from imbalanced data sets

By Nitesh V. Chawla, Nathalie Japkowicz, Aleksander Kotcz. 2004

2,064 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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In Information Systems

Citations in the corpus

Box drawings for learning with imbalanced data (2014, neutral): “I.2.6 [Artificial Intelligence]: Learning”
When Training and Test Sets are Different: Characterising Learning Transfer (2013, background): “In [5] the authors give an overview of the content of a number of workshops in this area, and the papers referenced provide an interesting overview of the field.”
A novel ensemble method for classifying imbalanced data (2015, background): “Therefore, researchers have paid much attention to the class imbalance problems, and several thematic workshops and conferences were held, such as the Association for the Advancement of Artificial Intelligence (AAAI) 2000 [7], the International Conference on Machine Learning (ICML) 2003 [8], and the”
Cost-sensitive learning methods for imbalanced data (2010, methodology): “Researchers have introduced many techniques to deal with class imbalance, as summarized in [1] and [2].”
An embedded feature selection method for imbalanced data classification (2019, background): “ROC is a curve that models the trade-off between TRP and FPR [36], [37], which is constructed in a two-dimensional space.”
A Systematic Literature Review on Fault Prediction Performance in Software Engineering (2012, neutral): “These papers are: [[8]], [[9]], [[10]], [[11]], [[18]], [[21]], [[29]], [[31]], [[32]], [[37]], [[51]], [[56]], [[69]], [[73]], [[74]], [[76]], [[83]], [[86]], [[92]], [[98]], [[109]], [[110]], [[116]], [[117]], [[118]], [[120]], [[122]], [[127]], [[133]], [[135]], [[154]], [[160]], [[163]], [[164]]”
Detecting adversarial advertisements in the wild (2011, background): “The vast majority of ads are from good-faith advertisers; thus detecting adversarial advertisements presents a difficult class imbalance issue [6].”
A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets (2008, background): “Recently the imbalanced data-set problem has demanded more attention in the field of machine learning research [5].”
Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning (2005, methodology): “1 The Methods at Data Level At the data level, different forms of re-sampling methods were proposed [1].”
Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images (2016, background): “Previous works have shown that adding more informative samples to the training set can improve the performance of the learner substantially [9], [10], [12]–[14], [45], [46].”
On handling negative transfer and imbalanced distributions in multiple source transfer learning (2014, background): “Although the issue of imbalanced distributions have been well studied in traditional classification [12,13], yet how to handle imbalanced distributions in source domains is a topic seldom discussed in the literature of transfer learning.”
A Survey on the Application of Genetic Programming to Classification (2010, background): “Although classification is one of the most researched tasks in computer science, some new issues still arise, like the problem of learning from imbalanced data [181], [182].”
Learned lessons in credit card fraud detection from a practitioner perspective (2014, methodology): “On the other hand, unsupervised methods do not use the class of transactions and are capable of detecting new fraudulent behaviours (Bolton & Hand, 2001).”
Outlier Analysis (2013, methodology): “This seems to be the case from many practical experiences with real data [102].”
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets (2015, background): “Class imbalance — a difference in the numbers of positive and negative instances, usually with the negatives outnumbering the positives — occurs in a wide range of scientific areas, including the life sciences, where unequal class distributions arise naturally [6 – 9].”
Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey (2009, background): “This problem is even more important in fields where the natural data sets are highly imbalanced in the first place (Chawla et al., 2004), like in the domain of previously unencountered malware detection.”