Exploring clusters of rare events using unsupervised random forests

• Main Authors: Z A Omar, Chin, Su Na, Siti Rahayu Mohd. Hashim, N Hamzah
• Format: Conference or Workshop Item
• Language: English; English
• Published: 2022
• Subjects: SD388 Forestry machinery and engineering
• Online Access: https://eprints.ums.edu.my/id/eprint/34420/1/FULL%20TEXT.pdf; https://eprints.ums.edu.my/id/eprint/34420/2/ABSTRACT.pdf; https://eprints.ums.edu.my/id/eprint/34420/; https://iopscience.iop.org/article/10.1088/1742-6596/2314/1/012019/pdf

Given highly imbalanced data, most learning algorithms face the challenge of accurately predicting rare events, while such cases are the ones that carry importance and useful knowledge. In a binary class label dataset, the rare events are the ones in the minority class. This study used a stroke data...