An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson's disease
暂无分享,去创建一个
Gang Wang | Sujing Wang | Wenbin Liu | Chao Ma | Hui-Ling Chen | Zhen-Nao Cai | G. Wang | Sujing Wang | Chao Ma | Wenbin Liu | Zhennao Cai | Huiling Chen
[1] Guang-Bin Huang,et al. Learning capability and storage capacity of two-hidden-layer feedforward networks , 2003, IEEE Trans. Neural Networks.
[2] M. Breteler,et al. Epidemiology of Parkinson's disease , 2006, The Lancet Neurology.
[3] Babak Shahbaba,et al. Nonlinear Models Using Dirichlet Process Mixtures , 2007, J. Mach. Learn. Res..
[4] Guang-Bin Huang,et al. Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions , 1998, IEEE Trans. Neural Networks.
[5] Chi Cheng,et al. Extreme learning machines for intrusion detection , 2012, The 2012 International Joint Conference on Neural Networks (IJCNN).
[6] Ronald J. Baken,et al. Clinical measurement of speech and voice , 1987 .
[7] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.
[8] Max A. Little,et al. Suitability of Dysphonia Measurements for Telemonitoring of Parkinson's Disease , 2008, IEEE Transactions on Biomedical Engineering.
[9] Neha Singh,et al. Advances in the treatment of Parkinson's disease , 2007, Progress in Neurobiology.
[10] C. Tanner,et al. Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030 , 2007, Neurology.
[11] Gang Wang,et al. An efficient diagnosis system for detection of Parkinson's disease using fuzzy k-nearest neighbor approach , 2013, Expert Syst. Appl..
[12] Tom Fawcett,et al. An introduction to ROC analysis , 2006, Pattern Recognit. Lett..
[13] Chee Kheong Siew,et al. Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.
[14] Pasi Luukka,et al. Feature selection using fuzzy entropy measures with similarity classifier , 2011, Expert Syst. Appl..
[15] Guang-Bin Huang,et al. Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).
[16] Nawwaf N. Kharma,et al. Advances in Detecting Parkinson's Disease , 2010, ICMB.
[17] Arif Gülten,et al. Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms , 2011, Comput. Methods Programs Biomed..
[18] Ian H. Witten,et al. Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .
[19] Cemal Köse,et al. A Statistical Segmentation Method for Measuring Age-Related Macular Degeneration in Retinal Fundus Images , 2010, Journal of Medical Systems.
[20] William H. Press,et al. The Art of Scientific Computing Second Edition , 1998 .
[21] อนิรุธ สืบสิงห์,et al. Data Mining Practical Machine Learning Tools and Techniques , 2014 .
[22] Larry A. Rendell,et al. A Practical Approach to Feature Selection , 1992, ML.
[23] Michael Frankfurter,et al. Numerical Recipes In C The Art Of Scientific Computing , 2016 .
[24] Tong Liu,et al. Effective detection of Parkinson's disease using an adaptive fuzzy k-nearest neighbor approach , 2013, Biomed. Signal Process. Control..
[25] Tong Liu,et al. A fast approach for detection of erythemato-squamous diseases based on extreme learning machine with maximum relevance minimum redundancy feature selection , 2015, Int. J. Syst. Sci..
[26] Lorene M Nelson,et al. Incidence of Parkinson's disease: variation by age, gender, and race/ethnicity. , 2003, American journal of epidemiology.
[27] Kemal Polat,et al. Classification of Parkinson's disease using feature weighting method on the basis of fuzzy C-means clustering , 2012, Int. J. Syst. Sci..
[28] M. Hariharan,et al. A new hybrid intelligent system for accurate detection of Parkinson's disease , 2014, Comput. Methods Programs Biomed..
[29] Timothy A. Warner,et al. Kernel-based extreme learning machine for remote-sensing image classification , 2013 .
[30] Murat Gök,et al. An ensemble of k-nearest neighbours algorithm for detection of Parkinson's disease , 2015, Int. J. Syst. Sci..
[31] Usama M. Fayyad,et al. Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.
[32] Der-Chiang Li,et al. A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets , 2011, Artif. Intell. Medicine.
[33] Resul Das,et al. A comparison of multiple classification methods for diagnosis of Parkinson disease , 2010, Expert Syst. Appl..
[34] Ian Witten,et al. Data Mining , 2000 .
[35] Fuhui Long,et al. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[36] J. Jankovic. Parkinson’s disease: clinical features and diagnosis , 2008, Journal of Neurology, Neurosurgery, and Psychiatry.
[37] João Paulo Papa,et al. Improving Parkinson's disease identification through evolutionary-based feature selection , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[38] Olcay Kursun,et al. Telediagnosis of Parkinson’s Disease Using Measurements of Dysphonia , 2010, Journal of Medical Systems.
[39] Tom Fawcett,et al. ROC Graphs: Notes and Practical Considerations for Researchers , 2007 .
[40] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[41] Hongming Zhou,et al. Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[42] P. Snyder,et al. Variability in fundamental frequency during speech in prodromal and incipient Parkinson's disease: A longitudinal case study , 2004, Brain and Cognition.
[43] R Iansek,et al. Speech impairment in a large sample of patients with Parkinson's disease. , 1999, Behavioural neurology.
[44] Ron Kohavi,et al. A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.
[45] Lufeng Hu,et al. An efficient machine learning approach for diagnosis of paraquat-poisoned patients , 2015, Comput. Biol. Medicine.
[46] Chih-Jen Lin,et al. A Practical Guide to Support Vector Classication , 2008 .
[47] Dayou Liu,et al. A Computer Aided Diagnosis System for Thyroid Disease Using Extreme Learning Machine , 2012, Journal of Medical Systems.
[48] Theodoros Damoulas,et al. Multiclass Relevance Vector Machines: Sparsity and Accuracy , 2010, IEEE Transactions on Neural Networks.
[49] Max A. Little,et al. Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease , 2012, IEEE Transactions on Biomedical Engineering.
[50] J. Massano,et al. Clinical approach to Parkinson's disease: features, diagnosis, and principles of management. , 2012, Cold Spring Harbor perspectives in medicine.
[51] Freddie Åström,et al. A parallel neural network approach to prediction of Parkinson's Disease , 2011, Expert Syst. Appl..