Deep centroid: a general deep cascade classifier for biomedical omics data classification.

ABSTRACT:

Motivation

Classification of samples using biomedical omics data is a widely used method in biomedical research. However, these datasets often possess challenging characteristics, including high dimensionality, limited sample sizes, and inherent biases across diverse sources. These factors limit the performance of traditional machine learning models, particularly when applied to independent datasets.

Results

To address these challenges, we propose a novel classifier, Deep Centroid, which combines the stability of the nearest centroid classifier and the strong fitting ability of the deep cascade strategy. Deep Centroid is an ensemble learning method with a multi-layer cascade structure, consisting of feature scanning and cascade learning stages that can dynamically adjust the

SUBMITTER: Xie K 

PROVIDER: S-EPMC10868341 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature