A genetic algorithm-based approach for class-imbalanced learning

Shangyan Dong; Yongcheng Wu

doi:10.1117/12.2501764

26 July 2018 A genetic algorithm-based approach for class-imbalanced learning

Shangyan Dong, Yongcheng Wu

Proceedings Volume 10828, Third International Workshop on Pattern Recognition; 108281D (2018) https://doi.org/10.1117/12.2501764
Event: Third International Workshop on Pattern Recognition, 2018, Jinan, China

Abstract

It is often the case for machine learning that datasets are imbalanced in the real world. When dealing with this problem, the traditional classification method aiming to maximize the overall accuracy of classification is not suitable. To tackle this issue and improve the performance of classifiers, methods based on oversampling, undersampling and cost-sensitive classification are widely employed. In this paper, we propose a new genetic algorithm-based over-sampling technique for class-imbalanced datasets. The genetic algorithm can create optimized synthetic minority class instances to produce a balanced training datasets. The experimental results on 5 class-imbalanced datasets show that our method performs better than three existing sampling techniques in terms of AUC and F-measure.

Citation Download Citation

Shangyan Dong and Yongcheng Wu "A genetic algorithm-based approach for class-imbalanced learning", Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 108281D (26 July 2018); https://doi.org/10.1117/12.2501764

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available