Application of support vector machines to classification of blood cells.
- Author:
Haojun WANG
1
;
Chongxun ZHENG
;
Ying LI
;
Huafeng ZHU
;
Xiangguo YAN
Author Information
1. Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an 710049.
- Publication Type:Journal Article
- MeSH:
Algorithms;
Blood Cells;
classification;
Cluster Analysis;
Computational Biology;
methods;
Humans;
In Vitro Techniques;
Least-Squares Analysis;
Models, Biological;
Nonlinear Dynamics
- From:
Journal of Biomedical Engineering
2003;20(3):484-487
- CountryChina
- Language:Chinese
-
Abstract:
The support vector machine (SVM) is a new learning technique based on the statistical learning theory. It was originally developed for two-class classification. In this paper, the SVM approach is extended to multi-class classification problems, a hierarchical SVM is applied to classify blood cells in different maturation stages from bone marrow. Based on stepwise decomposition, a hierarchical clustering method is presented to construct the architecture of the hierarchical (tree-like) SVM, then the optimal control parameters of SVM are determined by some criterion for each discriminant step. To verify the performances of classifiers, the SVM method is compared with three classical classifiers using 3-fold cross validation. The preliminary results indicate that the proposed method avoids the curse of dimensionality and has greater generalization. Thus, the method can improve the classification correctness for blood cells from bone marrow.
