A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Feature Selection for Support Vector Machines by Means of Genetic Algorithms
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Expert Systems with Applications: An International Journal
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A Computer Aided Diagnosis System for Thyroid Disease Using Extreme Learning Machine
Journal of Medical Systems
Self-adaptive local Fisher discriminant analysis for semi-supervised image recognition
International Journal of Biometrics
Hi-index | 12.05 |
In this paper, a novel hybrid method named the LFDA_SVM, which integrates a new feature extraction method and a classification algorithm, has been introduced for diagnosing hepatitis disease. The two integrated methods are the local fisher discriminant analysis (LFDA) and the supporting vector machine (SVM), respectively. In the proposed LFDA_SVM, the LFDA is employed as a feature extraction tool for dimensionality reduction in order to further improve the diagnostic accuracy of the standard SVM algorithm. The effectiveness of the LFDA_SVM has been rigorously evaluated against the hepatitis dataset, a benchmark dataset, from UCI Machine Learning Database in terms of classification accuracy, sensitivity and specificity respectively. In addition, the proposed LFDA_SVM has been compared with three existing methods including the SVM based on principle component analysis (PCA_SVM), the SVM based on fisher discriminant analysis (FDA_SVM) and the standard SVM in terms of their classification accuracy. Experimental results have demonstrated that the LFDA_SVM greatly outperforms other three methods. The best classification accuracy (96.77%) obtained by the LFDA_SVM is much higher than that of the compared ones. Promisingly, the proposed LFDA_SVM might serve as a new candidate of powerful methods for diagnosing hepatitis with excellent performance.