MetaCost: a general method for making classifiers cost-sensitive

By Pedro M. Domingos. 1999

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Citations in the corpus

Data preprocessing techniques for classification without discrimination (2011, methodology): “MetaCost [9] for making classifiers cost-sensitive by wrapping a cost minimizing procedure around them.”
SMOTE: Synthetic Minority Over-sampling Technique (2002, background): “Another approach that is similar to our work is that of Domingos (1999).”
A systematic study of the class imbalance problem in convolutional neural networks (2017, methodology): “Another group applies cost-minimizing procedure, like MetaCost which is a wrapper method to make classifiers cost sensitive [63].”
Finding a short and accurate decision rule in disjunctive normal form by exhaustive search (2010, positive): “Cost-insensitive algorithms can be made cost-sensitive by resampling or wrapper techniques (Elkan 2001; Viaene and Dedene 2005), such as MetaCost (Domingos 1999).”
Robust Classification for Imprecise Environments (2000, background): “There exist several dozen articles in which techniques for cost-sensitive learning are suggested (Turney, 1996), but few studies evaluate and compare them (Domingos, 1999; Pazzani, Merz, Murphy, Ali, Hume, & Brunk, 1994; Provost, Fawcett, & Kohavi, 1998).”
Training deep neural networks on imbalanced data sets (2016, background): “An optimal classifier can be learned by minimizing the objective function [12,13,23].”
Editorial: special issue on learning from imbalanced data sets (2004, background): “, [29][12]).”
Detecting duplicate web documents using clickthrough data (2011, methodology): “Not Duplicate 75 24 2408 To classify the queries, we observed if users who issue the query have a strong preference for exactly one URL, i.e. Table 6: MetaCost CART Confusion Matrix clicking on this one URL at least 60% of the time.”
On the Class Imbalance Problem (2008, methodology): “MetaCost [32] is another ensemble method.”
Sequential cost-sensitive decision making with reinforcement learning (2002, background): “A number of cost-sensitive learning methods have been developed [4, 17, 6] that have been shown to be superior to traditional”
Cost-sensitive learning by cost-proportionate example weighting (2003, methodology): “The second uses Bayes risk theory to assign each example to its lowest risk This author’s present address: Toyota Technological Institute at Chicago, 427 East 60th Street, Second Floor - Press Building, Chicago, IL 60637. class [2, 19, 14].”
Decision trees with minimal costs (2004, background): “Works such as (Domingos 1999, Elkan 2001 and Kai 1998) (Nunez, 1991; Tan 1993) considered the test cost alone without incorporating misclassification cost.”
A Novel Bayes Model: Hidden Naive Bayes (2009, background): “In this kind of cost-sensitive applications, accurate class probability estimation is even required [4].”
Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank (2017, methodology): “MetaCost classifier (Domingos, 1999): MetaCost is a meta-classifier that allows the use of different costs for penalising (e.”