BriefGPT.xyz
Jul, 2018
多类恶性预测的合成采样
Synthetic Sampling for Multi-Class Malignancy Prediction
HTML
PDF
Matthew Yung, Eli T. Brown, Alexander Rasin, Jacob D. Furst, Daniela S. Raicu
TL;DR
本文探讨了几种多标签不平衡分类问题中的过采样技术,证明了通过使用合成采样技术,可以提高恶性程度预测的每个类的性能敏感性,寻找合适的低层次图像特征和随机森林分类器对数据集进行分类,可以对多标签不平衡分类问题提供信息和指导。
Abstract
We explore several
oversampling techniques
for an
imbalanced multi-label classification
problem, a setting often encountered when developing models for
→