Dr. Miin-Shen Yang
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Dr. Miin-Shen Yang

Professor
Chung Yuan Christian University, Taiwan

Highest Degree
Ph.D. in Statistics from University of South Carolina, USA

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Biography

Dr. Miin-Shen Yang received the BS degree in Mathematics from the Chung Yuan Christian University, Chung-Li, Taiwan, in 1977, the MS degree in Applied Mathematics from the National Chiao-Tung University, Hsinchu, Taiwan, in 1980, and the PhD degree in Statistics from the University of South Carolina, Columbia, USA, in 1989. In 1989, he joined the faculty of the Department of Mathematics in the Chung Yuan Christian University (CYCU) as an Associate Professor, where, since 1994, he has been a Professor. From 1997 to 1998, he was a Visiting Professor with the Department of Industrial Engineering, University of Washington, Seattle, USA. During 2001-2005, he was the Chairman of the Department of Applied Mathematics in CYCU. Since 2012, he has been a Distinguished Professor of the Department of Applied Mathematics and the Director of Chaplain’s Office in CYCU. His research interests include Fuzzy Clustering, applications of Statistics, Neural Fuzzy Systems, Pattern Recognition, and Machine Learning. Dr. Yang was an Associate Editor of the IEEE Transactions on Fuzzy Systems (2005-2011), and is an Associate Editor of the Applied Computational Intelligence & Soft Computing and Editor-in-Chief of Advances in Computational Research. He has published 104 research articles in journals as well as 39 conference papers contributed as author/co-author.

Area of Interest:

Bayesian Inference
100%
Fuzzy Clustering
62%
Neural Fuzzy Systems
90%
Pattern Recognition
75%
Machine Learning
55%

Research Publications in Numbers

Books
0
Chapters
0
Articles
0
Abstracts
0

Selected Publications

  1. Yang, M.S. and Y.C. Tian, 2015. Bias-correction fuzzy clustering algorithms Inf. Sci., 309: 138-162.
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  2. Yang, M.S., Y.C. Tian and C.Y. Lin, 2014. Robust fuzzy classification maximum likelihood clustering with multivariate t-distributions. Int. J. Fuzzy Syst., 16: 566-576.
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  3. Yang, M.S., S.J. Chang-Chien and H.C. Kuo, 2014. On Mean Shift Clustering for Directional Data on a Hypersphere. Lect. Notes Comput. Sci., 8468: 809-818.
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  4. Kuo, H.C., M.S. Yang, J.H. Yang and Y.C. Chen, 2014. SCM-Driven Tree View for Microarray Data. Lect. Notes Comput. Sci., 10.1007/978-3-319-07176-3_19: 207-215.
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  5. Hwang, C.M. and M.S. Yang, 2014. New similarity measures between generalized trapezoidalfuzzy numbersusing the Jaccard index. Int. J. Uncertainty Fuzziness Knowl. Syst., 22: 831-844.
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  6. Yu, J., M.S. Yang and P. Hao, 2013. Clustering construction on a multimodal probability model. Inf. Sci., 237: 211-220.
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  7. Yang, M.S., C.Y. Lin and Y.C. Tian, 2013. A robust fuzzy classification maximum likelihood clustering framework. Int. J. Uncertainty Fuzziness Knowl. Syst., 21: 755-776.
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  8. Hwang, C.M., M.S. Yang, 2013. New construction for similarity measures between intuitionistic fuzzy sets based on lower, upper and middle fuzzy sets. Int. J. Fuzzy Syst., 15: 371-378.
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  9. Hwang, C.M. and M.S. Yang, 2013. New construction for similarity measures between intuitionistic fuzzy sets based onlower, upper and middle fuzzy sets. Int. J. Fuzzy Syst., 15: 359-366.
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  10. Yang, M.S., W.L. Hung, D.H. Chen, 2012. Self-organizing map for symbolic data. Fuzzy Sets Syst., 203: 49-73.
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  11. Yang, M.S., C.Y. Lai and C.Y. Lin, 2012. A robust EM clustering algorithm for Gaussian mixture models. Pattern Recognit. 45: 3950-3961.
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  12. Wen-Liang, H., S. Chang-Chien and M. Yang, 2012. Self-updating clustering algorithm for estimating the parameters in mixtures ofvon Mises distributions. J. Appli. Stat., 39: 2259-2274.
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  13. Tsai, H.S., W.L. Hung and M.S. Yang, 2012. A robust kernel-based fuzzy c-means algorithm by incorporating suppressed and magnified membership for MRI image segmentation. Artif. Intell. Comput. Intell., 7530: 744-754.
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  14. Hwang, C.M., M.S. Yang, W.L. Hung and M.G. Lee, 2012. A similarity measureof intuitionistic fuzzy sets based on Sugeno integral with its application to pattern recognition. Inf. Sci., 189: 93-109.
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  15. Hwang, C.M., M.S. Yang and W.L. Hung, 2012. On similarity, inclusion measure and entropy between type-2 fuzzy sets. Int. J. Uncertainty Fuzziness Knowledge Based Syst., 2012: 433-449.
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  16. Hwang, C.M. and M.S. Yang, 2012. Modified cosine similarity measure between intuitionistic fuzzy sets. Artif. Intell. Comput. Intell., 7530: 285-293.
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  17. Hunga, W.L., S.J. Chang-Chien and M.S. Yang, 2012. Self-updating clustering algorithm for estimating the parameters in mixtures of von Mises distributions. J. Appl. Stat., 39: 2259-2274.
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  18. Chang-Chien, S.J., W.L. Hung and M.S. Yang, 2012. On mean shift-based clustering for circular data. Soft Comput., 16: 1043-1060.
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  19. C.M. Hwang, M.S. Yang, W.L. Hung and M.G. Lee, 2012. A similarity measure of intuitionistic fuzzy sets based on the Sugeno integral with its application to pattern recognition. Inf. Sci., 189: 93-109.
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  20. Yu, J., M.S. Yang., E.S. Lee, 2011. Sample-weighted clustering methods. Comput. Math. Appl., 62: 2200-2208.
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  21. Yu, J., M.S. Yang and P. Hao, 2011. A novel multimodal probability model for cluster analysis. Fundam. Inf., 111: 81-90.
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  22. Yang, M.S. and C.Y. Lai, 2011. A robust automatic merging possibilistic clustering method. IEEE Trans. Fuzzy Syst., 19: 26-41.
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  23. Lai, C.Y. and M.S. Yang, 2011. Entropy-type classification maximum likelihood algorithms for mixture models. Soft Comput., 15: 373-381.
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  24. Hwang, C.M., M.S. Yang and W.L. Hung, 2011. Similarity, inclusion and entropy measures between type-2 fuzzy sets based on the sugeno integral. Math. Comput. Model., 53: 1788-1797.
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  25. Hung, W.L., M.S. Yang, J. Yu and C.M. Hwang, 2011. Feature-weighted mountain method with its applications to color image segmentation. Int. J. Comput. Intell. Syst., 4: 1002-1011.
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  26. Hung, W.L., M.S. Yang and E.S. Lee, 2011. Cell formation using fuzzy relational clustering algorithm. Math. Comput. Model., 53: 1776-1787.
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  27. Hung, W.L., D.H. Chen and M.S. Yang, 2011. Suppressed fuzzy-soft learning vector quantization for MRI segmentation. Artif. Intell. Med., 52: 33-43.
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  28. Yang, M.S., K.L. Wu, K.C.R. Lin, H.C. Liu and J.F. Lirng, 2010. On tree types of competitive learning algorithms with their comparisons and applications to MRI segmentation. Int. J. Intell. Syst., 25: 1018-1102.
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  29. Wu, K.L., M.S. Yang and J.N. Hsieh, 2010. Mountain c-regressions method Pattern Recognit., 43: 86-98.
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  30. Hwang, C.M. and M.S. Yang, 2010. On fuzzy renewal processes for fuzzy random variables and extended theorems. Int. J. Intell. Syst., 26: 115-128.
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  31. Hung, W.L., M.S. Yang, J. Yu and C.M. Hwang, 2010. Feature-weighted mountain method with its application to color image segmentation. Rough Sets Knowl. Technol., 6401: 537-544.
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  32. Hung, W.L., M.S. Yang and E.S. Lee, 2010. A robust clustering procedure for fuzzy data. Comput. Math. Appl., 60: 151-165.
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  33. Yu, J., M.S. Yang and P. Hao, 2009. A novel multimodal probability model for cluster analysis. Rough Sets Knowl. Technol., 5589: 397-404.
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  34. Yang, M.S. and D.C. Lin, 2009. On similarity and inclusion measures between type-2 fuzzy sets with an application to clustering. Comput. Math. Appl., 57: 896-907.
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  35. Wu, K.L., M.S. Yang and J.N. Hsieh, 2009. Robust cluster validity indexes. Pattern Recognit., 42: 2541-2550.
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  36. Po, R.W., Y.Y. Guh and M.S. Yang, 2009. A new clustering approach using data envelopment analysis. Eur. J. Oper. Res., 199: 276-284.
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  37. Guh, Y.Y., M.S. Yang, R.W. Po and E.S. Lee, 2009. Interval-valued fuzzy relation-based clustering with its application to performance evaluation. Comput. Math. Appl., 57: 841-849.
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  38. Yang, M.S., Y.H. Chiang, C.C. Chen and C.Y. Lai, 2008. A fuzzy k-partitions model for categorical data and its comparison to the GoM model. Fuzzy Sets Syst., 159: 390-405.
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  39. Yang, M.S., K.L. Wu, J.N. Hsieh and J. Yu, 2008. Alpha-cut implemented fuzzy clustering algorithms and switching regressions. IEEE Trans. Syst. Man Cybern., 38: 588-603.
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  40. Yang, M.S. and J.H. Yang, 2008. Machine-part cell formation in group technology using a modified ART1 method. Eur. J. Operat. Res., 188: 140-152.
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  41. Yang, M.S. and H.S. Tsai, 2008. A gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction. Pattern Recognition Lett., 29: 1713-1725.
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  42. Hwang, C.M. and M.S. Yang, 2008. On entropy of fuzzy sets. Int. J. Uncertainty, Fuzziness Knowl. Syst., 16: 519-527.
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  43. Hung, W.L., M.S. Yang and D.H. Chen, 2008. Bootstrapping approach to feature-weight selection in fuzzy c-means algorithms with an application in color image segmentation. Pattern Recognit. Lett., 29: 1317-1325.
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  44. Hung, W.L. and M.S. Yang, 2008. On the J-divergence of intuitionistic fuzzy sets with its application to pattern recognition. Inf. Sci., 178: 1641-1650.
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  45. Hung, W.L. and M.S. Yang, 2008. On similarity measures between intuitionistic fuzzy sets. Int. J. Intell. Syst., 23: 364-383.
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  46. Guh, Y.Y., M.S. Yang, R.W. Po and E.S. Lee, 2008. Establishing performance evaluation structures by fuzzy relation-based cluster analysis. Comput. Math. Appl., 56: 572-582.
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  47. Yu, J. and M.S. Yang, 2007. A generalized fuzzy clustering regularization model with optimality tests and model complexity analysis. IEEE Trans. Fuzzy Syst., 15: 904-904.
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  48. Wu, K.L. and M.S. Yang, 2007. Mean shift-based clustering. Pattern Recognit., 40: 3035-3052.
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  49. Hwang, C.M. and M.S. Yang, 2007. Generalization of belief and plausibility functions to fuzzy sets based on the sugeno integral. Int. J. Intell. Syst., 22: 1215-1228.
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  50. Hung, W.L. and M.S. Yang, 2007. Similarity measures of intuitionistic fuzzy sets based on Lp metric. Int. J. Approximate Reasoning, 2007: 120-136.
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  51. Yang, M.S., W.L. Hung and T.L. Chung, 2006. Alternative fuzzy clustering algorithms with L 1-norm and covariance matrix. Adv. Concepts Intell. Vision Syst., 4179: 654-665.
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  52. Yang, M.S., W.L. Hung and F.C. Cheng, 2006. Mixed-variable fuzzy clustering approach to part family and machine cell formation for GT applications. Int. J. Prod. Econ., 103: 185-198.
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  53. Yang, M.S., W.L. Hung and C.H.Chang, 2006. A penalized fuzzy clustering algorithm. WSEAS Trans. Comput. Res., 1: 83-88.
  54. Yang, M.S., K.C. Lin, H.C. Liu and J.F. Lirng, 2006. Magnetic resonance imaging segmentation techniques using batch-type learning vector quantization algorithms. Magn. Reson. Imaging, 25: 265-277.
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  55. Yang, M.S. and K.L. Wu, 2006. Unsupervised possibilistic clustering. Pattern Recogn., 39: 5-21.
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  56. Wu, K.L. and M.S. Yang, 2006. Alternative learning vector quantization. Pattern Recognition, 39: 351-362.
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  57. Jiang, I.G., L.C. Yeh, W.L. Hung and M.S. Yang, 2006. Data analysis on the extra-solar planets using robust clustering. Mon. Not. R. Astron. Soc., 370: 1379-1392.
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  58. Hung, W.L., M.S. Yang and D.H. Chen, 2006. Parameter selection for suppressed fuzzy c-means with an application to MRI segmentation. Pattern Recognit. Lett., 2006: 424-438.
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  59. Hung, W.L. and M.S. Yang, 2006. Fuzzy entropy on intuitionistic fuzzy sets. Int. J. Intell. Syst., 21: 443-451.
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  60. Hung, W.L. and M.S. Yang, 2006. An omission approach for detecting outliers in fuzzy regression models. Fuzzy Sets Syst., 157: 3109-3122.
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  61. Yu, J. and M.S. Yang, 2005. Optimality test for generalized FCM and its application to parameter selection. IEEE Trans. Fuzzy Syst., 13: 164-176.
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  62. Yang, M.S., W.L. Hung and S.J. Chang-Chien, 2005. On a similarity measure between LR-type fuzzy numbers and its application to database acquisition. Int. J. Intell. Syst., 20: 1001-1016.
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  63. Yang, M.S. and K.L. Wu, 2005. A modified mountain clustering algorithm. Pattern Anal. Applic., 8: 125-138.
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  64. Yang, M.S. and H.S. Tsai, 2005. An alternative fuzzy compactness and separation clustering algorithm. Adv. Concepts Intell. Vision Syst., 3708: 146-153.
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  65. Yang, M.S. and C.Y. Lai, 2005. Mixture Poisson regression models for heterogeneous count data based on latent and fuzzy class analysis. Soft Comput., 9: 519-524.
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  66. Yang, J.H. and N.Y. Yu, 2005. Estimation of parameters in latent class models using fuzzy clustering algorithms. Eur. J. Oper. Res., 160: 515-531.
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  67. Yang, J.H. and M.S. Yang, 2005. A control chart pattern recognition system using a statistical correlation coefficient method. Comput. Ind. Eng., 48: 205-221.
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  68. Wu, K.L., J. Yu and M.S. Yang, 2005. A novel fuzzy clustering algorithm based on a fuzzy scatter matrix with optimality tests. Pattern Recognit. Lett., 2005: 369-652.
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  69. Wu, K.L. and M.S. Yang, 2005. A cluster validity index for fuzzy clustering. Pattern Recognit. Lett., 26: 1275-1291.
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  70. Jiang, I.G., L.C. Yeh, W.L. Hung and M.S. Yang, 2005. A note on the ICS algorithm with corrections and theoretical analysis. IEEE Trans. Image Process., 14: 973-978.
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  71. Hung, W.L. and M.S. Yang, 2005. Fuzzy clustering on LR-type fuzzy numbers with an application in Taiwanese tea evaluation. Fuzzy Sets Syst. , 150: 561-577.
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  72. Yang, M.S., P.Y.Hwang and D.H.Chen, 2004. Fuzzy clustering algorithms formixed feature variables. Fuzzy Sets and Syst., 141: 301-317.
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  73. Yang, M.S., and H.M. Chen, 2004. Fuzzy class logistic regression analysis. Int. J. Uncertainty Fuzziness Knowledge Based Syst., 12: 761-780.
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  74. Yang, M.S., P.Y. Hwang and D.H. Chen, 2004. Fuzzy clusteringalgorithms for mixed feature variables. Fuzzy Sets Syst., 141: 301-317.
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  75. Yang, M.S. and K.L. Wu, 2004. A similarity-based robust clustering method. IEEE Trans. Pattern Anal. Mach. Intell., 26: 434-448.
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  76. Hung, W.L. and M.S. Yang, 2004. Similarity measures of intuitionistic fuzzy sets based on Hausdorff distance. Pattern Recognit. Lett., 25: 1603-1611.
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  77. Hung, W.L. and M.S. Yang, 2004. Similarity measures between type-2 fuzzy sets. Int. J. Uncertainty Fuzziness Knowledge Based Syst., 12: 827-841.
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  78. Huang, W.L. and M.S. Yang, 2004. Similarity measures of intuitionistic fuzzy sets based on Hausdorff distance. Pattern Recognit. Lett., 25: 1603-1611.
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  79. Hong, Y.Y., Z.T. Chao and M.S. Yang, 2004. A fuzzy multiple linear regression based loss formula in electric distribution systems. Fuzzy Sets Syst., 142: 293-306.
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  80. Yu, J. and M.S. Yang, 2003. A study on a generalized FCM. Rough Sets Fuzzy Sets Data Min. Granular Comput., 2639: 390-393.
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  81. Yang, M.S., T.S. Chen and K.L. Wu, 2003. Generalized belief function, plausibility function, and Dempster`s combinational rule to fuzzy sets. Int. J. Intell. Syst., 18: 925-937.
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  82. Yang, M.S. and H.H. Liu, 2003. Fuzzy least-square algorithms for interactive fuzzy linear regression models. Fuzzy Sets and Syst., 135: 305-316.
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  83. Wu, K.L. and M.S. Yang, 2003. A fuzzy-soft learning vector quantization. Neurocomputing, 55: 681-697.
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  84. Lin, K.C., M.S. Yang, H.C. Liu, J.F. Lirng and P.N. Wang, 2003. Generalized Kohonen`s competitive learning algorithms for ophthalmological MR image segmentation. Magn. Reson. Imaging, 21: 863-870.
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  85. Yanga, M.S., Y.J. Hua, K.C.R. Linb and C.C.L. Linc, 2002. Segmentation techniques for tissue differentiation in MRI of Ophthalmology using fuzzy clustering algos. Magnetic Resonance Imag., 20: 173-179.
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  86. Yang, M.S. and T.S. Lin, 2002. Fuzzy least-square linear regression analysis for fuzzy input-output data. Fuzzy Sets and Syst., 126: 389-399.
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  87. Yang, M.S. and J.H. Yang, 2002. A fuzzy-soft learning vector quantization for control chart pattern recognition. Int. J. Prod. Res., 40: 2721-2731.
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  88. Yang, J.H. and T.S. Lin, 2002. Fuzzy least-squares linear regression analysis for fuzzy input-output data. Fuzzy Sets Syst., 126: 389-399.
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  89. Wu, K.L. and M.S. Yang, 2002. Alternative c-means clustering algorithms. Pattern Recognit., 35: 2267-2278.
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  90. Yang, J.H. and H.M. Shih, 2001. Cluster analysis based on fuzzy relations. Fuzzy Sets Syst., 120: 197-212.
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  91. Yang, M.S. and H.C. Liu, 1999. Fuzzy clustering procedures for conical fuzzy vector data. Fuzzy Sets Syst., 106: 189-200.
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  92. Yang, M.S., and C.H. Chen, 1998. On the edited fuzzy K-nearest neighbor rule. IEEE Trans. Syst. Man Cybern. Part B Cybern., 28: 461-466.
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  93. Yang, M.S. and M.C. Liu, 1998. On possibility analysis of fuzzy data. Fuzzy Sets Syst., 94: 171-183.
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  94. Yang, M.S. amd C.H. Ko, 1997. On cluster-wise fuzzy regression analysis. IEEE Trans. Syst. Man Cybern. Part B Cybern., 27: 1-13.
    PubMed  |  Direct Link  |  
  95. Yang, J.H. and J.A. Pan, 1997. On fuzzy clustering of directional data. Fuzzy Sets Syst., 91: 319-326.
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  96. Yang, M.S. amd C.H. Ko, 1996. On a class of fuzzy c-numbers clustering procedures for fuzzy data. Fuzzy Sets Syst., 84: 49-60.
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  97. Yang, M.S., 1994. On asymptotic normality of a class of fuzzy c-means clustering procedures. Int. J. Gen. Syst., 22: 391-403.
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  98. Yang, M.S., and C.T. Chen, 1994. Convergence rate of the fuzzy generalized nearest neighbor rule. Comput. Math. Appl., 27: 1-8.
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  99. Yang, M.S. amd C.F. Su, 1994. On parameter estimation for normal mixtures based on fuzzy clustering algorithms. Fuzzy Sets Syst., 68: 13-28.
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  100. Yang, M.S., 1993. On a class of fuzzy classification maximum likelihood procedures. Fuzzy Sets Syst., 57: 365-375.
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  101. Yang, M.S., 1993. Convergence properties of the generalized fuzzy c-means clustering algorithms. Comput. Math. Appl., 25: 3-11.
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  102. Yang, M.S., 1993. A survey of fuzzy clustering. Math. Comput. Modell., 18: 1-16.
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  103. Yang, M.S., and C.T. Chen, 1993. On strong consistency of the fuzzy generalized nearest neighbor rule. Fuzzy Sets Syst., 60: 273-281.
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  104. Yang, M.S. and K.F. Yu, 1992. On existence and strong consistency of a class of fuzzy c-means clustering procedures. Cybern. Syst. Int. J., 23: 583-602.
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  105. Yang, M.S. and K.F. Yu, 1990. On stochastic convergence theorems for the fuzzy c-means clustering procedure. Int. J. Gen. Syst., 16: 397-411.
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