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Published in:   Vol. 3 Issue 2 Date of Publication:   December 2014

A Review Paper on Outlier Detection using Two-Phase SVM Classifiers with Cross Training Approach for Multi- Disease Diagnosis

Kalpit R. Chandpa,Jignasa N. Patel

Page(s):   55-59 ISSN:   2278-2397
DOI:   10.20894/IJDMTA.102.003.002.003 Publisher:   Integrated Intelligent Research (IIR)


  1. Jiawei. Han and Micheline Kamber,   "Data Mining: Concepts and Techniques",   pdf, 2nd Edition
    View Artical

  2. Varun Chandola, Arindam Banerjee and Vipin Kumar,   "Outlier Detection: A Survey.pdf
    View Artical

  3. Dr. Shuchita Upadhyaya and Karanjit Singh,   "2.3 Classification Based Outlier Detection Techniques",   International Journal of Computer Trends and Techn   ,Vol.3   ,Issue 2   ,2012
    View Artical

  4. Wu, Shih-Hung,   "Support Vector Machine Tutorial.pdf
    View Artical

  5. Jason Weston,   "Support Vector Machine and Statistical Learning Theory Tutorial.pdf
    View Artical

  6. Chih-Wei Hsu, Chih-Chung Chang ,and Chih-Jen Lin,   "A Practical Guide to Support Vector Classication.pdf
    View Artical

  7. M. Syed Mohamed and T. Kavitha,   "Outlier Detection Using Support Vector Machine in Wireless Sensor Network Real Time Data",   International Journal of Soft Computing and Engine   ,Vol.1   ,Issue 2   ,2011
    View Artical

  8. B.Scholkopf ,R.C.Williamson, A.J. Smola, J.Shawe-Taylor, and J. C.Platt,   "Support vector method for novelty detection",   in Advances in Neural Information Processing Syste   ,2000
    View Artical

  9. B. E. Boser, I. M. Guyon, and V. N. Vapnik,   "A training algorithm for optimal margin classifiers",   in COLT?92: Proceedings of the fifth annual worksh   ,1992
    View Artical

  10. K. Rama Lakshmi and S.Prem Kumar,   "Utilization of Data Mining Techniques for Prediction and Diagnosis of Major Life Threatening Disease",   International Journal of Scientific & Engineering    ,Vol.4   ,Issue 6   ,2013
    View Artical

  11. Karanjit Singh and Dr. Shuchita Upadhyaya,   "Outlier Detection:Applications And Techniques",   International Journal of Computer Science Issues   ,Vol.9   ,Issue 1   ,2013
    View Artical

  12. Suya Xu, Caiping Hu, Lisong Wang, Guobin Zhang,   "Support Vector Machines based on K-Nearest Neighbor Algorithm for Outlier Detection in WSNs",   IEEE   ,2012
    View Artical

  13. Xiaoqi Peng, Jun Chen, Hongyuan Shen,   "Outlier Detection Method Based on SVM and Its Application in Copper-matte Converting",   IEEE   ,2010
    View Artical

  14. Shang Gao and Hongmei Li,   "Breast Cancer Diagnosis Based on Support Vector Machine",   IEEE International Conference on Uncertainty Reaso   ,2012
    View Artical

  15. Esraa M. Hashem, Mai S. Mabrouk,   "A Study of Support Vector Machine Algorithm for Liver Disease Diagnosis",   American Journal of Intelligent Systems   ,2014
    View Artical

  16. Edward Smart, David Brown and Luke Axel-Berg,   "Comparing One and Two Class Classification Methods for Multiple Fault Detection on an Induction Moto",   IEEE Symposium on Industrial Electronics & Applica
    View Artical

  17. Jyoti Soni, Ujma Ansari, Dipesh Sharma, Sunita Soni,   "Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction",   International Journal of Computer science and Engi   ,Vol.3   ,Issue 6   ,2011
    View Artical

  18. Choi J.P., Han T.H. and Park R.W,   "A Hybrid Bayesian Network Model for Predicting Breast Cancer Prognosis",   J Korean Soc Med Inform   ,2009
    View Artical

  19. C. Hattice and K. Metin,,   "A Diagnostic Software tool for Skin Diseases with Basic and Weighted K-NN",   Innovations in Intelligent Systems and Application   ,2012
    View Artical

  20. O. Er, N. Yumusakc and F. Temurtas,   "Chest diseases diagnosis using artificial neural networks",   Expert Systems with Applications   ,Vol.37   ,2010
    View Artical

  21. J. Escudero, J. P. Zajicek and E. Ifeachor,   "Early Detection and Characterization of Alzheimer?s Disease in linical Scenarios Using Bioprofile Co",   33rd Annual International Conference of the IEEE E   ,2011
    View Artical

  22. Binod Kumar Mishra, Prashant Lakkadwala, Naveen Kumar Shrivastava,   "Novel Approach to Predict CARDIOVASCULAR DISEASE using Incremental SVM",   IEEE International Conference on Communication Sys   ,2013
    View Artical

  23. Mona Y. Elshinawya, Abdel-Hameed A. Badwy, Wael W.AbdelMageed, Mohamed F. Chauikha,   "Comparing One-class and Two-class SVM Classifiers for Normal Mamogram Detection.pdf
    View Artical

  24. Stephan Dreiseit, Melanie Os, Christian Scheibbock Michael Binder,   "AMIA Symposium 2010
    View Artical