Comparison of Two Output-Coding Strategies for Multi-Class Tumor Classification Using Gene Expression Data and Latent Variable Model as Binary Classifier
Multi-class cancer classification based on microarray data is described. A generalized output-coding scheme based on One Versus One (OVO) combined with Latent Variable Model (LVM) is used. Results from the proposed One Versus One (OVO) output- coding strategy is compared with the results obtained fr...
Main Authors: | Sandeep J. Joseph, Kelly R. Robbins, Wensheng Zhang, Romdhane Rekaya |
---|---|
Format: | Article |
Language: | English |
Published: |
SAGE Publishing
2010-03-01
|
Series: | Cancer Informatics |
Online Access: | http://la-press.com/comparison-of-two-output-coding-strategies-for-multi-class-tumor-class-a1909 |
Similar Items
-
Comparison of Two Output-Coding Strategies for Multi-Class Tumor Classification Using Gene Expression Data and Latent Variable Model as Binary Classifier
by: Sandeep J. Joseph, et al.
Published: (2010-01-01) -
A jackknife-like method for classification and uncertainty assessment of multi-category tumor samples using gene expression information
by: Bertrand Keith, et al.
Published: (2010-04-01) -
Ant colony algorithm for analysis of gene interaction in high-dimensional association data Algoritmo colônia de formigas para análise de interação gênica em dados de associação de alta dimensão
by: Romdhane Rekaya, et al.
Published: (2009-07-01) -
Deep Neural Networks Classification via Binary Error-Detecting Output Codes
by: Martin Klimo, et al.
Published: (2021-04-01) -
Binary Output Layer of Feedforward Neural Networks for Solving Multi-Class Classification Problems
by: Sibo Yang, et al.
Published: (2019-01-01)