The nature of statistical learning theory
The nature of statistical learning theory
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Comparing Classifiers: Pitfalls toAvoid and a Recommended Approach
Data Mining and Knowledge Discovery
A Simple Decomposition Method for Support Vector Machines
Machine Learning
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
An SVM-based Algorithm for Identification of Photosynthesis-specific Genome Features
CSB '03 Proceedings of the IEEE Computer Society Conference on Bioinformatics
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
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Expert Systems with Applications: An International Journal
Estimating the utility value of individual credit card delinquents
Expert Systems with Applications: An International Journal
Statistical analysis of mammographic features and its classification using support vector machine
Expert Systems with Applications: An International Journal
Application of a 3NN+1 based CBR system to segmentation of the notebook computers market
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Combination of feature selection approaches with SVM in credit scoring
Expert Systems with Applications: An International Journal
An SVM-based machine learning method for accurate internet traffic classification
Information Systems Frontiers
A hybrid prediction model with F-score feature selection for type II Diabetes databases
Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India
Journal of Medical Systems
Quantitative characterisation of Plasmodium vivax in infected erythrocytes: a textural approach
International Journal of Artificial Intelligence and Soft Computing
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Breast cancer is a serious problem for the young women of Taiwan. Some medical researches have proved that DNA viruses are one of the high-risk factors closely related to human cancers. Five DNA viruses are studied in this research: specific types of HSV-1 (herpes simplex virus type 1), EBV (Epstein-Barr virus), CMV (cytomegalovirus), HPV (human papillomavirus), and HHV-8 (human herpesvirus-8). The purposes of this study are to obtain the bioinformatics about breast tumor and DNA viruses, and to build an accurate diagnosis model about breast cancer and fibroadenoma. Research efforts have reported with increasing confirmation that the support vector machine (SVM) has a greater accurate diagnosis ability. Therefore, this study constructs a hybrid SVM-based strategy with feature selection to render a diagnosis between the breast cancer and fibroadenoma and to find the important risk factor for breast cancer. The results show that {HSV-1, HHV-8} or {HSV-1, HHV-8, CMV} are the most important features and that the diagnosis model achieved high classification accuracy, at 86% of average overall hit rate. A Linear discriminate analysis (LDA) diagnosis model is also constructed in this study. The LDA model shows that {HSV-1, HHV-8, EBV} or {HSV-1, HHV-8} are significant factors which are similar to that of the SVM-based classifier. However, the classificatory accuracy of the SVM-based classifier is slightly better than that of LDA in the negative hit ratio, positive hit ratio, and overall hit ratio.