Imbalanced Learning with Parametric Linear Programming Support Vector Machine for Weather Data Application

This repository is the source code for the paper implemented in Python. The link to access the paper: https://link.springer.com/article/10.1007/s42979-020-00381-y

Abstarct

Imbalanced learning is an aspect of predictive modeling and machine learning that has taken a lot of attention in the last decade. Due to the nature of rare events, finding a reliable and efficient classification method for imbalanced data set has been challenging, and multiple research projects have been carried out to improve the existing algorithms for more accurate predictions of such data sets. To this end, we propose a Linear Programming Support Vector Machine (LP-SVM) applicable to imbalanced data. Apart from model selection and modifications, we have also implemented a parameter selection method based on the parametric simplex approach for parameter tuning of LP-SVM. For numerical tests, we have used a real data set consisting of weather observations made by Bureau of Meteorology's (BM) system in Australia, and the results show that the proposed method works pretty well on the tested examples.