Examining characteristics of predictive models with imbalanced big data

Examining characteristics of predictive models with imbalanced big data

Abstract High class imbalance between majority and minority classes in datasets can skew the performance of Machine Learning algorithms and bias predictions in favor of the majority (negative) class. This bias, for cases where the minority (positive) class is of greater interest and the occurrence o...

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Bibliographic Details
Main Authors: Tawfiq Hasanin, Taghi M. Khoshgoftaar, Joffrey L. Leevy, Naeem Seliya
Format: Article
Language:English
Published: SpringerOpen 2019-07-01
Series:Journal of Big Data
Subjects:
Big data
Feature Importance
Feature Selection
Class Imbalance
Machine Learning
Random Undersampling
Online Access:http://link.springer.com/article/10.1186/s40537-019-0231-2

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http://link.springer.com/article/10.1186/s40537-019-0231-2

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