Support Vector Machine Based Diagnostic System for Breast Cancer Using Swarm Intelligence
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Gang Wang | Sujing Wang | Dayou Liu | Jie Liu | Bo Yang | Hui-Ling Chen | G. Wang | Sujing Wang | Bo Yang | Da-you Liu | Jie Liu | Huiling Chen
[1] M. Cevdet Ince,et al. An expert system for detection of breast cancer based on association rules and neural network , 2009, Expert Syst. Appl..
[2] Constantin F. Aliferis,et al. GEMS: A system for automated cancer diagnosis and biomarker discovery from microarray gene expression data , 2005, Int. J. Medical Informatics.
[3] Elif Derya íbeyli. Implementing automated diagnostic systems for breast cancer detection , 2007 .
[4] Maurice Clerc,et al. The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..
[5] Mehmet Fatih Akay,et al. Support vector machines combined with feature selection for breast cancer diagnosis , 2009, Expert Syst. Appl..
[6] Mei-Ling Huang,et al. Neural Network Classifier with Entropy Based Feature Selection on Breast Cancer Diagnosis , 2010, Journal of Medical Systems.
[7] Moshe Sipper,et al. A fuzzy-genetic approach to breast cancer diagnosis , 1999, Artif. Intell. Medicine.
[8] Pei-Chann Chang,et al. A hybrid model combining case-based reasoning and fuzzy decision tree for medical data classification , 2011, Appl. Soft Comput..
[9] Vennila Ramalingam,et al. Breast mass classification based on cytological patterns using RBFNN and SVM , 2009, Expert Syst. Appl..
[10] Steven Salzberg,et al. On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach , 1997, Data Mining and Knowledge Discovery.
[11] James Kennedy,et al. Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.
[12] Kemal Polat,et al. A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis , 2007, Comput. Biol. Medicine.
[13] Yue Shi,et al. A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).
[14] Kemal Polat,et al. Breast cancer diagnosis using least square support vector machine , 2007, Digit. Signal Process..
[15] Rudy Setiono,et al. Generating concise and accurate classification rules for breast cancer diagnosis , 2000, Artif. Intell. Medicine.
[16] J. Ross Quinlan,et al. Improved Use of Continuous Attributes in C4.5 , 1996, J. Artif. Intell. Res..
[17] Chih-Jen Lin,et al. Combining SVMs with Various Feature Selection Strategies , 2006, Feature Extraction.
[18] Nello Cristianini,et al. Kernel Methods for Pattern Analysis , 2004 .
[19] Rudolf Kruse,et al. Obtaining interpretable fuzzy classification rules from medical data , 1999, Artif. Intell. Medicine.
[20] Thorsten Joachims,et al. Text categorization with support vector machines , 1999 .
[21] Chih-Jen Lin,et al. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.
[22] K. Bennett,et al. A support vector machine approach to decision trees , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).
[23] Dayou Liu,et al. A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis , 2011, Expert Syst. Appl..
[24] Joel Quintanilla-Domínguez,et al. WBCD breast cancer database classification applying artificial metaplasticity neural network , 2011, Expert Syst. Appl..
[25] Bernhard Schölkopf,et al. Feature selection for support vector machines by means of genetic algorithm , 2003, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.
[26] Elif Derya Übeyli. A Mixture of Experts Network Structure for Breast Cancer Diagnosis , 2005, Journal of Medical Systems.
[27] Ferenc Szeifert,et al. Supervised fuzzy clustering for the identification of fuzzy classifiers , 2003, Pattern Recognit. Lett..
[28] Russell C. Eberhart,et al. A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.
[29] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[31] R. Eberhart,et al. Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).
[32] Chih-Jen Lin,et al. A Practical Guide to Support Vector Classication , 2008 .
[33] Saman K. Halgamuge,et al. Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients , 2004, IEEE Transactions on Evolutionary Computation.
[34] Federico Girosi,et al. Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
[35] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[36] Russell C. Eberhart,et al. A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.
[37] Elif Derya Übeyli. Adaptive Neuro-Fuzzy Inference Systems for Automatic Detection of Breast Cancer , 2009, Journal of Medical Systems.
[38] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[39] Thorsten Joachims,et al. Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.
[40] Nello Cristianini,et al. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .
[41] Yuhui Shi,et al. Particle swarm optimization: developments, applications and resources , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).
[42] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[43] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[44] S. Sathiya Keerthi,et al. Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms , 2002, IEEE Trans. Neural Networks.
[45] Ron Kohavi,et al. Irrelevant Features and the Subset Selection Problem , 1994, ICML.
[46] Lois Boggess,et al. ARTIFICIAL IMMUNE SYSTEM CLASSIFICATION OF MULTIPLE- CLASS PROBLEMS , 2002 .