Evaluation and classification of otoneurological data with new data analysis methods based on machine learning

Information Sciences

Volumn 177, Issue 9, 2007, Pages 1963-1976

  Siermala, Markku ^a   Juhola, Martti ^b   Laurikkala, Jorma ^b   Iltanen, Kati ^b   Kentala, Erna ^c   Pyykkö, Ilmari ^d  

^a UNIVERSITY OF TAMPERE   (Finland)

^b TAMPERE UNIVERSITY OF TECHNOLOGY   (Finland)

^c HELSINKI UNIVERSITY CENTRAL HOSPITAL   (Finland)

^d TAMPERE UNIVERSITY HOSPITAL   (Finland)

Author keywords

Classification; Neural networks; Otoneurology; Variable evaluation

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA STRUCTURES; DISEASES; LEARNING SYSTEMS; NEURAL NETWORKS; NEUROLOGY; SPECTRUM ANALYSIS;

CLASS DISTRIBUTION EFFECTS; MULTILAYER PERCEPTION NETWORKS; OTONEUROLOGY; VARIABLE EVALUATION;

DATA REDUCTION;

EID: 33847257326     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2006.11.002     Document Type: Article

References (26)

1. Blum A.L., and Langley P. Selection of relevant features and examples in machine learning. Artificial Intelligence 97 (1997) 245-271
2. Castellano G., and Fanelli A.M. Variable selection using neural-network models. Neurocomputing 31 (2000) 1-13
3. Doyle H.R., Parmanto B., Munro P.W., Marino I.R., Aldrighetti L., Doria C., McMichael J., and Fung J.J. Building clinical classifiers using incomplete observations - a neural network ensemble for hepatoma detection in patients with cirrhosis. Methods of Information in Medicine 34 (1995) 253-258
4. Féraud R., and Clérot F. A methodology to explain neural network classification. Neural Networks 17 (2002) 237-246
5. Hart A., and Wyatt J. Evaluating black-boxes as medical decision aids: issues arising from a study of neural networks. Medical Informatics 15 3 (1990) 229-236
6. Hsu W.H. Genetic wrappers for feature selection in decision tree induction and variable ordering in Bayesian network structure learning. Information Sciences 163 (2004) 103-122
7. Jerez-Aragonés J.M., Gómez-Ruiz J.A., Ramos-Jiménez G., Munoz-Pérez J., and Alba-Conejo E. A combined neural network and decision trees model for prognosis of breast cancer relapse. Artificial Intelligence in Medicine 27 (2003) 45-63
8. Juhola M., and Laurikkala J. Increasing accuracy of neural network classification with principal component analysis for otoneurological and female urinary incontinence data. CR-ROM of XVIIIth International Congress of the European Federation for Medical Informatics, Saint Malo, France (2003), IOS Press, Amsterdam
9. Kentala E., Pyykkö I., Auramo Y., and Juhola M. Database for vertigo. Otolaryngology - Head and Neck Surgery 112 (1995) 383-390
10. Kentala E., Pyykkö I., Auramo Y., and Juhola M. Otoneurological expert system. Annals of Otology Rhinology and Laryngology 105 (1996) 654-658
11. van de Laar P., and Heskes T. Input selection based on an ensemble. Neurocomputing 34 (2001) 227-238
12. Laurikkala J., Kentala E., Juhola M., Pyykkö I., and Lammi S. Usefulness of imputation for the analysis of incomplete otoneurological data. International Journal of Medical Informatics 58-59 (2001) 235-242
13. Neal R.M. Assessing relevance determination methods using DELVE. In: Bishop C.M. (Ed). Neural Networks and Machine Learning (1998), Springer Verlag, Berlin 97-129
14. Pesonen E., Eskelinen M., and Juhola M. Treatment of missing data values in a neural network based decision support system for acute abdominal pain. Artificial Intelligence in Medicine 13 (1998) 139-146
15. Pyle D. Data Preparation for Data Mining (1999), Morgan Kaufman, San Francisco
16. Schulerud H., and Albregtsen F. Many are called, but few are chosen, feature selection and error estimation in high dimensional spaces. Computer Methods and Programs in Biomedicine 73 (2004) 91-99
17. Selamat A., and Omatu S. Web page feature selection and classification using neural networks. Information Sciences 158 (2004) 69-88
18. Siermala M., Juhola M., and Vihinen M. On postprocessing of neural network prediction of polyproline type II secondary structures: network spectrum, response analysis, and scattering. Neural Computing & Applications 11 (2003) 238-243
19. Straszecka E. Combining uncertainty and imprecision in models of medical diagnosis. Information Sciences 176 (2006) 3026-3059
20. Swingler K. Applying Neural Networks, A Practical Guide (1996), Academic Press, London
21. Trappemberg T., Ouyang J., and Back A. Input variable selection: mutual information and linear mixing measures. IEEE Transactions on Knowledge and data Engineering 18 (2006) 37-46
22. Viallefont V., Raftery A.E., and Richardson S. Variable selection and Bayesian model averaging in case-control studies. Statistics in Medicine 20 (2001) 3215-3230
23. Viikki K., Juhola M., Pyykkö I., and Honkavaara P. Evaluating training data suitability for decision tree induction. Journal of Medical Systems 25 (2001) 133-144
24. Witten I.H., and Frank E. Data Mining (2000), Morgan Kaufman Publishers, San Francisco
25. Wolf L., and Shashua A. Feature selection for unsupervised and supervised inference: the emergence of sparsity in a weight-based approach. Journal of Machine Learning Research 6 (2005) 1855-1887
26. Yan J., Zhang B., Yan S., Liu N., Yang Q., Cheng Q., Li H., Chen Z., and Ma W.-Y. A scalable supervised algorithm for dimensionality reduction on streaming data. Information Sciences 176 (2006) 2042-2065