Multiclass support vector machines for diagnosis of erythemato-squamous diseases

Expert Systems with Applications

Volumn 35, Issue 4, 2008, Pages 1733-1740

  Übeyli, Elif Derya ^a  

^a TOBB UNIVERSITY OF ECONOMICS AND TECHNOLOGY   (Turkey)

Author keywords

Error correcting output codes (ECOC); Erythemato squamous diseases; Multiclass support vector machine (SVM); Recurrent neural network (RNN)

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLASSIFIERS; GEARS; IMAGE CLASSIFICATION; IMAGE RETRIEVAL; LEARNING SYSTEMS; MULTILAYER NEURAL NETWORKS; NEURAL NETWORKS; RECURRENT NEURAL NETWORKS; SYSTEMS ANALYSIS; VECTORS;

CLASSIFICATION ACCURACIES; CLASSIFICATION SCHEME; ERROR CORRECTING OUTPUT CODES (ECOC); ERROR-CORRECTING OUTPUT CODES; ERYTHEMATO-SQUAMOUS DISEASES; MULTI CLASS; MULTI-CLASS SUPPORT VECTOR MACHINES; MULTI-LAYER PERCEPTRON NEURAL NETWORKS; MULTICLASS SUPPORT VECTOR MACHINE; MULTICLASS SUPPORT VECTOR MACHINE (SVM); NEW APPROACHES; RECURRENT NEURAL NETWORK (RNN); TRAINING SETS;

SUPPORT VECTOR MACHINES;

EID: 48749124392     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2007.08.067     Document Type: Article

References (24)

1. Chaudhuri B.B., and Bhattacharya U. Efficient training and improved performance of multilayer perceptron in pattern classification. Neurocomputing 34 (2000) 11-27
2. Cortes C., and Vapnik V. Support vector networks. Machine Learning 20 3 (1995) 273-297
3. Dietterich T.G., and Bakiri G. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research 2 (1995) 263-286
4. Elman J.L. Finding structure in time. Cognitive Science 14 2 (1990) 179-211
5. Güler I., and Übeyli E.D. Detection of ophthalmic artery stenosis by least-mean squares backpropagation neural network. Computers in Biology and Medicine 33 4 (2003) 333-343
6. Güler N.F., Übeyli E.D., and Güler I. Recurrent neural networks employing Lyapunov exponents for EEG signals classification. Expert Systems with Applications 29 3 (2005) 506-514
7. Güvenir H.A., Demiröz G., and İlter N. Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals. Artificial Intelligence in Medicine 13 (1998) 147-165
8. Güvenir H.A., and Emeksiz N. An expert system for the differential diagnosis of erythemato-squamous diseases. Expert Systems with Applications 18 1 (2000) 39-43
9. Hagan M.T., and Menhaj M.B. Training feedforward networks with the Marquardt algorithm. IEEE Transactions on Neural Networks 5 6 (1994) 989-993
10. Haykin S. Neural networks: A comprehensive foundation (1994), Macmillan, New York
11. Itchhaporia D., Snow P.B., Almassy R.J., and Oetgen W.J. Artificial neural networks: Current status in cardiovascular medicine. Journal of the American College of Cardiology 28 2 (1996) 515-521
12. Kordylewski H., Graupe D., and Liu K. A novel large-memory neural network as an aid in medical diagnosis applications. IEEE Transactions on Information Technology in Biomedicine 5 3 (2001) 202-209
13. Kwak N., and Choi C.-H. Input feature selection for classification problems. IEEE Transactions on Neural Networks 13 1 (2002) 143-159
14. Lim C.P., Harrison R.F., and Kennedy R.L. Application of autonomous neural network systems to medical pattern classification tasks. Artificial Intelligence in Medicine 11 (1997) 215-239
15. Miller A.S., Blott B.H., and Hames T.K. Review of neural network applications in medical imaging and signal processing. Medical & Biological Engineering & Computing 30 (1992) 449-464
16. Nanni L. An ensemble of classifiers for the diagnosis of erythemato-squamous diseases. Neurocomputing 69 (2006) 842-845
17. Petrosian A.A., Prokhorov D.V., Lajara-Nanson W., and Schiffer R.B. Recurrent neural network-based approach for early recognition of Alzheimer's disease in EEG. Clinical Neurophysiology 112 8 (2001) 1378-1387
18. Pineda F.J. Generalization of back-propagation to recurrent neural networks. Physical Review Letters 59 19 (1987) 2229-2232
19. Shieh J.-S., Chou C.-F., Huang S.-J., and Kao M.-C. Intracranial pressure model in intensive care unit using a simple recurrent neural network through time. Neurocomputing 57 (2004) 239-256
20. Tafeit E., and Reibnegger G. Artificial neural networks in laboratory medicine and medical outcome prediction. Clinical Chemistry and Laboratory Medicine 37 9 (1999) 845-853
21. Übeyli E.D., and Güler I. Neural network analysis of internal carotid arterial Doppler signals: Predictions of stenosis and occlusion. Expert Systems with Applications 25 1 (2003) 1-13
22. Übeyli E.D., and Güler I. Feature extraction from Doppler ultrasound signals for automated diagnostic systems. Computers in Biology and Medicine 35 9 (2005) 735-764
23. Übeyli E.D., and Güler I. Automatic detection of erythemato-squamous diseases using adaptive neuro-fuzzy inference systems. Computers in Biology and Medicine 35 5 (2005) 421-433
24. Vapnik V. The Nature of Statistical Learning Theory (1995), Springer-Verlag, New York