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A cross-validation framework to find a better state than the balanced one for oversampling in imbalanced classification
Imbalance classification has always been a popular research point in the application of
machine learning, data mining and pattern recognition. At present, there are also many …
machine learning, data mining and pattern recognition. At present, there are also many …
Assessing the Effect of Data Complexity and Instance Overlap Issues on Imbalanced Learning
Most machine learning (ML) algorithms work best when the samples in each class are
almost equal. However, if a dataset has imbalanced samples then the ML model can …
almost equal. However, if a dataset has imbalanced samples then the ML model can …
Simulating complexity measures on imbalanced datasets
Classification tasks using imbalanced datasets are not challenging on their own.
Classification models perform poorly on the minority class when the datasets present other …
Classification models perform poorly on the minority class when the datasets present other …
A General Framework for Finding the Optimal Imbalance Ratio in Sampling Methods
J Peng, Y Shao, L Huang - 2022 IEEE 5th International …, 2022 - ieeexplore.ieee.org
How to obtain better classification results from imbalance data has always been a research
hot spot in the neighborhood of machine learning and data mining. At present, there are …
hot spot in the neighborhood of machine learning and data mining. At present, there are …
面向高维混合不**衡信贷数据的单类分类方法.
张东梅， 买日旦， 吾守尔 - Journal of Computer …, 2021 - search.ebscohost.com
为实现对高维混合, 不**衡信贷数据中的不良贷款者的准确预测, 从降维预处理和分类算法两
方面进行优化, 提出一种基于混合数据主成分分析(Principal Component Analysis of Mixed …
方面进行优化, 提出一种基于混合数据主成分分析(Principal Component Analysis of Mixed …