Mining with rarity: a unifying framework

By Gary M. Weiss. 2004

1,509 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, Information Systems

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Citations in the corpus

On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products (2016, background): “sample [41], a safe fail mechanism is to always go for manual examination.”
A systematic study of the class imbalance problem in convolutional neural networks (2017, background): “However, it has some significant and long acknowledged limitations, particularly in the context of imbalanced datasets [92, 93, 94].”
RUSBoost: Improving classification performance when training data is skewed (2008, neutral): “Two such techniques are data sampling and boosting [9].”
Interpreting and Unifying Outlier Scores (2011, methodology): “In the supervised context of classification, much effort has been spent on methods of sanitizing imbalanced class prior probabilities for training as well as for evaluation of classifiers [7,24,44,53].”
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering (2009, background): “The two strategies that are commonly used to solve the problem are sampling to re-balance the data [3, 11] and cost-sensitive learning [8, 19].”
Combating the Small Sample Class Imbalance Problem Using Feature Selection (2010, background): “Also, Weiss reviewed these approaches in SIGKDD Explorations [11].”
ADASYN: Adaptive synthetic sampling approach for imbalanced learning (2008, background): “Rare instances, on the other hand, concerns itself with situations where data representing a particular event is limited compared to other distributions [4] [5], such as the detection of oil spills from satellite images [6].”
On handling negative transfer and imbalanced distributions in multiple source transfer learning (2014, neutral): “[38] proposed to vary”
Cost-sensitive boosting for classification of imbalanced data (2007, background): “[8–10 , • Imbalanced class distribution : The imbalance degree of a class distribution can be denoted by the ratio of the sample size of the small class to that of the prevalent class.”
Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images (2015, positive): “Oversampling (34) and sample weighting (35) are two solutions to address this problem.”
Engineering safety in machine learning (2016, background): “For a rare combination of features in a test sample [20], a safe fail mechanism is to always go for manual examination.”