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A Safe Zone SMOTE Oversampling Algorithm Used in Earthquake Prediction Based on Extreme Imbalanced Precursor Data.
- Source :
- International Journal of Pattern Recognition & Artificial Intelligence; Oct2021, Vol. 35 Issue 13, p1-18, 18p
- Publication Year :
- 2021
-
Abstract
- Earthquake prediction based on extreme imbalanced precursor data is a challenging task for standard algorithms. Since even if an area is in an earthquake-prone zone, the proportion of days with earthquakes per year is still a minority. The general method is to generate more artificial data for the minority class that is the earthquake occurrence data. But the most popular oversampling methods generate synthetic samples along line segments that join minority class instances, which is not suitable for earthquake precursor data. In this paper, we propose a Safe Zone Synthetic Minority Oversampling Technique (SZ-SMOTE) oversampling method as an enhancement of the SMOTE data generation mechanism. SZ-SMOTE generates synthetic samples with a concentration mechanism in the hyper-sphere area around each selected minority instances. The performance of SZ-SMOTE is compared against no oversampling, SMOTE and its popular modifications adaptive synthetic sampling (ADASYN) and borderline SMOTE (B-SMOTE) on six different classifiers. The experiment results show that the quality of earthquake prediction using SZ-SMOTE as oversampling algorithm significantly outperforms that of using the other oversampling algorithms. [ABSTRACT FROM AUTHOR]
- Subjects :
- EARTHQUAKE prediction
ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 02180014
- Volume :
- 35
- Issue :
- 13
- Database :
- Complementary Index
- Journal :
- International Journal of Pattern Recognition & Artificial Intelligence
- Publication Type :
- Academic Journal
- Accession number :
- 154254933
- Full Text :
- https://doi.org/10.1142/S0218001421550132