no code implementations • 10 Sep 2020 • Liran Shen, Meng Joo Er, Qingbo Yin
The Classification on high-dimension low-sample-size data (HDLSS) is a challenging problem and it is common to have class-imbalanced data in most application fields.
no code implementations • 21 Jun 2020 • Liran Shen, Meng Joo Er, Qingbo Yin
In this paper, we propose a novel classification criterion on HDLSS, tolerance similarity, which emphasizes the maximization of within-class variance on the premise of class separability.
no code implementations • 5 Jan 2019 • Qingbo Yin, Ehsan Adeli, Liran Shen, Dinggang Shen
Various applications in different fields, such as gene expression analysis or computer vision, suffer from data sets with high-dimensional low-sample-size (HDLSS), which has posed significant challenges for standard statistical and modern machine learning methods.