Classification of the fragrance properties of chemical compounds based on support vector machine and linear discriminant analysis

2008 ◽  
Vol 23 (4) ◽  
pp. 232-238 ◽  
Author(s):  
F. Luan ◽  
H. T. Liu ◽  
Y. Y. Wen ◽  
X. Y. Zhang
Keyword(s):  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Chemical Compounds ◽  
Support Vector ◽  
Linear Discriminant

scholarly journals Discriminating Quasars from Stars Based on SDSS and UKIDSS Databases

2012 ◽  
Vol 8 (S295) ◽  
pp. 180-180
Author(s):  
He Ma ◽  
Yanxia Zhang ◽  
Yongheng Zhao ◽  
Bo Zhang
Keyword(s):  
Support Vector Machines ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Experimental Result ◽  
Support Vector ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Joint Approach

AbstractIn this work, two different algorithms: Linear Discriminant Analysis (LDA) and Support Vector Machines (SVMs) are combined for the classification of unresolved sources from SDSS DR8 and UKIDSS DR8. The experimental result shows that this joint approach is effective for our case.

scholarly journals Raman Microspectral Study and Classification of the Pathological Evolution of Breast Cancer Using Both Principal Component Analysis-Linear Discriminant Analysis and Principal Component Analysis-Support Vector Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Heping Li ◽  
Yu Ren ◽  
Fan Yu ◽  
Dongliang Song ◽  
Lizhe Zhu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Principal Component Analysis ◽  
Support Vector Machine ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Ductal Carcinoma ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Linear Discriminant

To facilitate the enhanced reliability of Raman-based tumor detection and analytical methodologies, an ex vivo Raman spectral investigation was conducted to identify distinct compositional information of healthy (H), ductal carcinoma in situ (DCIS), and invasive ductal carcinoma (IDC). Then, principal component analysis-linear discriminant analysis (PCA-LDA) and principal component analysis-support vector machine (PCA-SVM) models were constructed for distinguishing spectral features among different tissue groups. Spectral analysis highlighted differences in levels of unsaturated and saturated lipids, carotenoids, protein, and nucleic acid between healthy and cancerous tissue and variations in the levels of nucleic acid, protein, and phenylalanine between DCIS and IDC. Both classification models were principal component analysis-linear discriminant analysis to be extremely efficient on discriminating tissue pathological types with 99% accuracy for PCA-LDA and 100%, 100%, and 96.7% for PCA-SVM analysis based on linear kernel, polynomial kernel, and radial basis function (RBF), respectively, while PCA-SVM algorithm greatly simplified the complexity of calculation without sacrificing performance. The present study demonstrates that Raman spectroscopy combined with multivariate analysis technology has considerable potential for improving the efficiency and performance of breast cancer diagnosis.

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