Title

Detecting Non-injured Passengers and Drivers in Car Accidents: A New Under-resampling Method for Imbalanced Classification

Authors

Son Nguyen, Bryant UniversityFollow
Gao Niu, Bryant UniversityFollow
John Thomas Quinn, Bryant UniversityFollow
Alan Olinsky, Bryant UniversityFollow
Jonathan Ormsbee
Richard M. SmithFollow
James BishopFollow

Document Type

Book Chapter

Comments

Is part of Advances in Business Management Forecasting.

Keywords

imbalanced data; resampling; under-sampling; classification; random forest; rare event detection; imbalance learning

Identifier Data

https://doi.org/10.4018/978-1-5225-3142-5

Publisher

Emerald Insight

Publication Source

Advances in Business and Management Forecasting, Volume 13

Abstract

We then propose a new procedure to resample the data. Our method is based on the idea of eliminating “easy” majority observations before under-sampling them. It has further improved the balanced accuracy of the Random Forest to 83.7%, making it the best approach for the imbalanced data.