On Combining Feature Selection and Over-Sampling Techniques for Breast Cancer Prediction

On Combining Feature Selection and Over-Sampling Techniques for Breast Cancer Prediction

Breast cancer prediction datasets are usually class imbalanced, where the number of data samples in the malignant and benign patient classes are significantly different. Over-sampling techniques can be used to re-balance the datasets to construct more effective prediction models. Moreover, some rela...

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Bibliographic Details
Main Authors:	Min-Wei Huang, Chien-Hung Chiu, Chih-Fong Tsai, Wei-Chao Lin
Format:	Article
Language:	English
Published:	MDPI AG 2021-07-01
Series:	Applied Sciences
Subjects:	breast cancer data mining machine learning feature selection over-sampling class imbalance
Online Access:	https://www.mdpi.com/2076-3417/11/14/6574

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