A study of the behaviour of linguistic fuzzy rule based classification systems in the framework of imbalanced data-sets

By Alberto Fernández, Salvador Garcia, M. J. Jesús, Francisco Herrera. Fuzzy Sets Syst., 2008

323 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/aebe0313b3507b81e1fa4dd28ca80f636a5d5073

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In Artificial Intelligence

Citations in the corpus

A Survey of Predictive Modeling on Imbalanced Domains (2016, methodology): “Applying a method that changes the data distribution to obtain a more balanced one is an effective solution to the imbalance problem [Estabrooks et al. 2004; Batuwita and Palade 2010a; Fern´andez et al. 2008, 2010].”
IP102: A Large-Scale Benchmark Dataset for Insect Pest Recognition (2019, background): “Table 1 also shows that the dataset has high imbalance ratio (IR) ( i.e ., higher than 9 IR [12]) at most super-class level of the IP102.”
Performance Evaluation of Machine Learning Algorithms in Post-operative Life Expectancy in the Lung Cancer Patients (2015, background): “…(Negative instances) or both; or b) algorithmic level adjusting classifier to be trained by modifying the class cost, establishing a bias towards the positive class, learning from just one class (recognition based learning) instead of learning from two classes (discrimination based learning) [26].”
Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. Open problems on intrinsic data characteristics (2012, methodology): “The class imbalance is dominant in a high number of real problems including, but not limited to, telecommunications, WWW, finances, ecology, biology, medicine and so on.”
Classification with class imbalance problem: A review (2015, background): “Fuzzy linguistic [114] investigated the behaviour of linguistic fuzzy rule based classification systems for imbalanced datasets while [115] proposed for a novel neuro-fuzzy network algorithm to produce multiple decision rules for real world banking data.”
SMOTE-RSB*: a hybrid preprocessing approach based on oversampling and undersampling for high imbalanced data-sets using SMOTE and rough sets theory (2012, background): “Partition was carried out in such a way that the quantity of elements in each class remained uniform [13].”