Classification of the Carcinogenicity of N-Nitroso Compounds Based on Support Vector Machines and Linear Discriminant Analysis

Citations Over TimeTop 10% of 2005 papers

Abstract

The support vector machine (SVM), as a novel type of learning machine, was used to develop a classification model of carcinogenic properties of 148 N-nitroso compounds. The seven descriptors calculated solely from the molecular structures of compounds selected by forward stepwise linear discriminant analysis (LDA) were used as inputs of the SVM model. The obtained results confirmed the discriminative capacity of the calculated descriptors. The result of SVM (total accuracy of 95.2%) is better than that of LDA (total accuracy of 89.8%).

Related Papers

• → Face recognition based on 2D Fisherface approach(2005)90 cited
• → Multiview discriminative learning for age-invariant face recognition(2013)37 cited
• → High discriminative SIFT feature and feature pair selection to improve the bag of visual words model(2017)9 cited
• → Face Recognition Using Fisher Linear Discriminant Analysis and Support Vector Machine(2009)3 cited
• → Gabor feature based support vector guided dictionary learning for hyperspectral image classification(2017)1 cited