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Volumn 29, Issue 1, 2011, Pages 203-221

A general framework for designing a fuzzy rule-based classifier

Author keywords

Classifier; Evolving SOM tree; Fuzzy rule; Genetic algorithm; Knowledge extraction; Variable selection

Indexed keywords


EID: 80053296433     PISSN: 02191377     EISSN: 02193116     Source Type: Journal    
DOI: 10.1007/s10115-010-0340-x     Document Type: Article
Times cited : (21)

References (67)
  • 3
    • 0001224048 scopus 로고    scopus 로고
    • Sparse bayesian learning and the relevance vector machine
    • Tipping ME (2001) Sparse bayesian learning and the relevance vector machine. J Mach Learn Res 1: 211-244.
    • (2001) J Mach Learn Res , vol.1 , pp. 211-244
    • Tipping, M.E.1
  • 5
    • 77954953206 scopus 로고    scopus 로고
    • Explanation and reliability of prediction models: the case of breast cancer recurrence
    • Strumbelj E, Bosnic Z, Kononenko I, Zakotnik B, Kuhar CG (2010) Explanation and reliability of prediction models: the case of breast cancer recurrence. Knowl Inf Syst 24(2): 305-324.
    • (2010) Knowl Inf Syst , vol.24 , Issue.2 , pp. 305-324
    • Strumbelj, E.1    Bosnic, Z.2    Kononenko, I.3    Zakotnik, B.4    Kuhar, C.G.5
  • 7
    • 0042591522 scopus 로고    scopus 로고
    • A complete fuzzy decision tree technique
    • Olaru C, Wehenkel L (2003) A complete fuzzy decision tree technique. Fuzzy Sets Syst 138: 221-254.
    • (2003) Fuzzy Sets Syst , vol.138 , pp. 221-254
    • Olaru, C.1    Wehenkel, L.2
  • 8
    • 0027601884 scopus 로고
    • Anfis: adaptive-network-based fuzzy inference system
    • Jang JR (1993) anfis: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23: 665-685.
    • (1993) IEEE Trans Syst Man Cybern , vol.23 , pp. 665-685
    • Jang, J.R.1
  • 9
    • 0026923589 scopus 로고
    • Fuzzy artmap: a neural network architecture for incremental supervised learning of analog multidimensional maps
    • Carpenter GA, Grossberg S, Markuzon N, Reynolds JH, Rosen DB (1992) Fuzzy artmap: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans Neural Netw 3(5): 698-713.
    • (1992) IEEE Trans Neural Netw , vol.3 , Issue.5 , pp. 698-713
    • Carpenter, G.A.1    Grossberg, S.2    Markuzon, N.3    Reynolds, J.H.4    Rosen, D.B.5
  • 10
    • 0026927202 scopus 로고
    • Fuzzy min-max neural networks-part 1: classification
    • Simpson KP (1992) Fuzzy min-max neural networks-part 1: classification. IEEE Trans Neural Netw 3(5): 776-786.
    • (1992) IEEE Trans Neural Netw , vol.3 , Issue.5 , pp. 776-786
    • Simpson, K.P.1
  • 11
    • 0029242750 scopus 로고
    • A method for fuzzy rules extraction directly from numerical data and its application to parten classification
    • Abe S, Lan MS (1995) A method for fuzzy rules extraction directly from numerical data and its application to parten classification. IEEE Trans Fuzzy Syst 3(1): 18-28.
    • (1995) IEEE Trans Fuzzy Syst , vol.3 , Issue.1 , pp. 18-28
    • Abe, S.1    Lan, M.S.2
  • 12
    • 0025198791 scopus 로고
    • Fuzzy sets in pattern recognition: methodology and methods
    • Pedrycz W (1990) Fuzzy sets in pattern recognition: methodology and methods. Pattern Recognit 23(1-2): 121-146.
    • (1990) Pattern Recognit , vol.23 , Issue.1-2 , pp. 121-146
    • Pedrycz, W.1
  • 13
    • 77953915351 scopus 로고    scopus 로고
    • Fuzzy entropy based optimization of clusters for the segmentation of lungs in ct scanned images
    • Jaffar MA, Hussain A, Mirza AM (2010) Fuzzy entropy based optimization of clusters for the segmentation of lungs in ct scanned images. Knowl Inf Syst 24(1): 91-111.
    • (2010) Knowl Inf Syst , vol.24 , Issue.1 , pp. 91-111
    • Jaffar, M.A.1    Hussain, A.2    Mirza, A.M.3
  • 14
    • 70350521692 scopus 로고    scopus 로고
    • Data mining of vector-item patterns using neighborhood histograms
    • Denton AM, Wu J (2009) Data mining of vector-item patterns using neighborhood histograms. Knowl Inf Syst 21: 173-199.
    • (2009) Knowl Inf Syst , vol.21 , pp. 173-199
    • Denton, A.M.1    Wu, J.2
  • 15
    • 0021892282 scopus 로고
    • Fuzzy identification of systems and its applications to modeling and control
    • Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15(1): 116-132.
    • (1985) IEEE Trans Syst Man Cybern , vol.15 , Issue.1 , pp. 116-132
    • Takagi, T.1    Sugeno, M.2
  • 16
    • 77957572578 scopus 로고    scopus 로고
    • Resource service optimal-selection based on intuitionistic fuzzy set and non-functionality qos in manufacturing grid system
    • doi: 10. 1007/s10115-009-0263-6
    • Tao F, Zhao D, Zhang L (2009) Resource service optimal-selection based on intuitionistic fuzzy set and non-functionality qos in manufacturing grid system. Knowl Inf Syst doi: 10. 1007/s10115-009-0263-6.
    • (2009) Knowl Inf Syst
    • Tao, F.1    Zhao, D.2    Zhang, L.3
  • 17
    • 0031164649 scopus 로고    scopus 로고
    • Modeling and control of hierarchical systems with fuzzy systems
    • Wang LX (1997) Modeling and control of hierarchical systems with fuzzy systems. Automatica 33(6): 1041-1053.
    • (1997) Automatica , vol.33 , Issue.6 , pp. 1041-1053
    • Wang, L.X.1
  • 21
    • 0025489075 scopus 로고
    • The self-organizing map
    • Kohonen T (1990) The self-organizing map. Proc IEEE 78(9): 1461-1480.
    • (1990) Proc IEEE , vol.78 , Issue.9 , pp. 1461-1480
    • Kohonen, T.1
  • 22
    • 0004683439 scopus 로고
    • Construction of fuzzy models through clustering techniques
    • Yoshinari Y, Pedrycz W, Hirota K (1993) Construction of fuzzy models through clustering techniques. Fuzzy Sets Syst 54(2): 157-165.
    • (1993) Fuzzy Sets Syst , vol.54 , Issue.2 , pp. 157-165
    • Yoshinari, Y.1    Pedrycz, W.2    Hirota, K.3
  • 23
    • 0002834406 scopus 로고    scopus 로고
    • Self-organizing neural-network based fuzzy system
    • Wang Y, Rong GA (1999) self-organizing neural-network based fuzzy system. Fuzzy Sets Syst 103: 1-11.
    • (1999) Fuzzy Sets Syst , vol.103 , pp. 1-11
    • Wang, Y.1    Rong, G.A.2
  • 24
    • 0035481747 scopus 로고    scopus 로고
    • A systematic neuro-fuzzy modeling framework with application to material property prediction
    • Chen MY, Linkens DA (2001) A systematic neuro-fuzzy modeling framework with application to material property prediction. IEEE Trans Syst Man Cybern Part B 31(5): 781-790.
    • (2001) IEEE Trans Syst Man Cybern Part B , vol.31 , Issue.5 , pp. 781-790
    • Chen, M.Y.1    Linkens, D.A.2
  • 26
    • 34547681970 scopus 로고    scopus 로고
    • Fuzzy classifier design using genetic algorithms
    • Zhou E, Khotanzad A (2007) Fuzzy classifier design using genetic algorithms. Pattern Recognit 40(12): 3401-3414.
    • (2007) Pattern Recognit , vol.40 , Issue.12 , pp. 3401-3414
    • Zhou, E.1    Khotanzad, A.2
  • 27
    • 0031588795 scopus 로고    scopus 로고
    • Fuzzy inference neural network
    • Nishina T, Hagiwara M (1997) Fuzzy inference neural network. Neurocomputing 14: 223-239.
    • (1997) Neurocomputing , vol.14 , pp. 223-239
    • Nishina, T.1    Hagiwara, M.2
  • 28
    • 30344446393 scopus 로고    scopus 로고
    • Combining som and fuzzy rule base for flow time prediction in semiconductor manufacturing factory
    • Chang PC, Liao TW (2006) Combining som and fuzzy rule base for flow time prediction in semiconductor manufacturing factory. Appl Soft Comput 6: 198-206.
    • (2006) Appl Soft Comput , vol.6 , pp. 198-206
    • Chang, P.C.1    Liao, T.W.2
  • 29
    • 0035670764 scopus 로고    scopus 로고
    • Evolving fuzzy neural networks for supervised/unsupervised on-line, knowledge-based learning
    • Kasabov N (2001) Evolving fuzzy neural networks for supervised/unsupervised on-line, knowledge-based learning. IEEE Trans Syst Man Cybern 31(6): 902-918.
    • (2001) IEEE Trans Syst Man Cybern , vol.31 , Issue.6 , pp. 902-918
    • Kasabov, N.1
  • 30
    • 54449087782 scopus 로고    scopus 로고
    • Clustering and co-evolution to construct network ensembles: an experimental study
    • Minku FL, Ludermir TB (2008) Clustering and co-evolution to construct network ensembles: an experimental study. Neural Netw 21: 1363-1379.
    • (2008) Neural Netw , vol.21 , pp. 1363-1379
    • Minku, F.L.1    Ludermir, T.B.2
  • 31
    • 0036435523 scopus 로고    scopus 로고
    • Data-driven generation of compact, accurate, and linguistically sound fuzzy classifiers based on a decision-tree initialization
    • Abonyi J, Roubos JA, Szeifert F (2003) Data-driven generation of compact, accurate, and linguistically sound fuzzy classifiers based on a decision-tree initialization. Int J Approx Reason 32(1): 1-21.
    • (2003) Int J Approx Reason , vol.32 , Issue.1 , pp. 1-21
    • Abonyi, J.1    Roubos, J.A.2    Szeifert, F.3
  • 32
    • 0033728494 scopus 로고    scopus 로고
    • Hierarchical fuzzy partition for pattern classification with fuzzy if-then rules
    • Kbir MM, Benkirane H, Maalmi K, Benslimane R (2000) Hierarchical fuzzy partition for pattern classification with fuzzy if-then rules. Pattern Recognit Lett 21: 503-509.
    • (2000) Pattern Recognit Lett , vol.21 , pp. 503-509
    • Kbir, M.M.1    Benkirane, H.2    Maalmi, K.3    Benslimane, R.4
  • 33
    • 33846816451 scopus 로고    scopus 로고
    • Identification of interpretable and accurate fuzzy classifiers and function estimators with hybrid methods
    • Pulkkinen P, Koivisto H (2007) Identification of interpretable and accurate fuzzy classifiers and function estimators with hybrid methods. Appl Soft Comput 7: 433-520.
    • (2007) Appl Soft Comput , vol.7 , pp. 433-520
    • Pulkkinen, P.1    Koivisto, H.2
  • 34
    • 0036721934 scopus 로고    scopus 로고
    • Feature selection with neural networks
    • Verikas A, Bacauskiene M (2002) Feature selection with neural networks. Pattern Recognit Lett 23(11): 1323-1335.
    • (2002) Pattern Recognit Lett , vol.23 , Issue.11 , pp. 1323-1335
    • Verikas, A.1    Bacauskiene, M.2
  • 35
    • 0037361892 scopus 로고    scopus 로고
    • Learning fuzzy classification rules from labeled data
    • Roubos JA, Setnes M, Abonyi J (2003) Learning fuzzy classification rules from labeled data. Inf Sci 150: 77-93.
    • (2003) Inf Sci , vol.150 , pp. 77-93
    • Roubos, J.A.1    Setnes, M.2    Abonyi, J.3
  • 36
    • 0032960792 scopus 로고    scopus 로고
    • Obtaining interpretable fuzzy classification rules from medical data
    • Nauck D, Kruse R (1999) Obtaining interpretable fuzzy classification rules from medical data. Artif Intell Med 16(2): 149-169.
    • (1999) Artif Intell Med , vol.16 , Issue.2 , pp. 149-169
    • Nauck, D.1    Kruse, R.2
  • 37
    • 0000888528 scopus 로고
    • A neural fuzzy system with linguistic teachnig signals
    • Lin CT, Lu YC (1995) A neural fuzzy system with linguistic teachnig signals. IEEE Trans Fuzzy Syst 3(2): 169-189.
    • (1995) IEEE Trans Fuzzy Syst , vol.3 , Issue.2 , pp. 169-189
    • Lin, C.T.1    Lu, Y.C.2
  • 38
    • 1942423173 scopus 로고    scopus 로고
    • Genso: a novel neural-fuzzy based early warning system for predicting bank failures
    • Tung WL, Quek C, Cheng P (2004) Genso: a novel neural-fuzzy based early warning system for predicting bank failures. Neural Netw 17: 567-587.
    • (2004) Neural Netw , vol.17 , pp. 567-587
    • Tung, W.L.1    Quek, C.2    Cheng, P.3
  • 39
    • 0030214780 scopus 로고    scopus 로고
    • Adaptive fuzzy rule-based classification systems
    • Nozaki K, Ishibuchi H, Tanaka H (1996) Adaptive fuzzy rule-based classification systems. IEEE Trans Fuzzy Syst 4(3): 238-250.
    • (1996) IEEE Trans Fuzzy Syst , vol.4 , Issue.3 , pp. 238-250
    • Nozaki, K.1    Ishibuchi, H.2    Tanaka, H.3
  • 40
    • 0032207749 scopus 로고    scopus 로고
    • Integrating fuzzy knowledge by genetic algorithms
    • Wang CH, Hong TP, Tseng SS (1998) Integrating fuzzy knowledge by genetic algorithms. IEEE Trans Evol Comput 2(4): 138-149.
    • (1998) IEEE Trans Evol Comput , vol.2 , Issue.4 , pp. 138-149
    • Wang, C.H.1    Hong, T.P.2    Tseng, S.S.3
  • 41
    • 34247636165 scopus 로고    scopus 로고
    • Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection
    • Tsang CH, Kwong S, Wang H (2007) Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection. Pattern Recognit 40(9): 2373-2391.
    • (2007) Pattern Recognit , vol.40 , Issue.9 , pp. 2373-2391
    • Tsang, C.H.1    Kwong, S.2    Wang, H.3
  • 42
    • 33750512162 scopus 로고    scopus 로고
    • Intrusion detection by integrating boosting genetic fuzzy classifier and data mining criteria for rule pre-screening
    • Ozyer T, Alhajj R, Barker K (2007) Intrusion detection by integrating boosting genetic fuzzy classifier and data mining criteria for rule pre-screening. J Netw Comput Appl 30(1): 99-113.
    • (2007) J Netw Comput Appl , vol.30 , Issue.1 , pp. 99-113
    • Ozyer, T.1    Alhajj, R.2    Barker, K.3
  • 43
    • 0347411204 scopus 로고    scopus 로고
    • Combining boosting and evolutionary algorithms for learning of fuzzy classification rules
    • Hoffmann F (2004) Combining boosting and evolutionary algorithms for learning of fuzzy classification rules. Fuzzy Sets Syst 141: 47-58.
    • (2004) Fuzzy Sets Syst , vol.141 , pp. 47-58
    • Hoffmann, F.1
  • 44
    • 0000582194 scopus 로고    scopus 로고
    • Integrating membership functions and fuzzy rule sets from multiple knowledge sources
    • Wang CH, Hong TP, Tseng SS (2000) Integrating membership functions and fuzzy rule sets from multiple knowledge sources. Fuzzy Sets Syst 112: 141-154.
    • (2000) Fuzzy Sets Syst , vol.112 , pp. 141-154
    • Wang, C.H.1    Hong, T.P.2    Tseng, S.S.3
  • 45
    • 56949090674 scopus 로고    scopus 로고
    • Automatic generation of fuzzy inference systems via unsupervised learning
    • Er MJ, Zhou Y (2008) Automatic generation of fuzzy inference systems via unsupervised learning. Neural Netw 21(10): 1556-1566.
    • (2008) Neural Netw , vol.21 , Issue.10 , pp. 1556-1566
    • Er, M.J.1    Zhou, Y.2
  • 46
    • 40549086646 scopus 로고    scopus 로고
    • Cluster-based evaluation in fuzzy-genetic data mining
    • Chen CH, Tseng VS, Hong TP (2008) Cluster-based evaluation in fuzzy-genetic data mining. IEEE Trans Fuzzy Syst 16(1): 249-262.
    • (2008) IEEE Trans Fuzzy Syst , vol.16 , Issue.1 , pp. 249-262
    • Chen, C.H.1    Tseng, V.S.2    Hong, T.P.3
  • 47
    • 33845212542 scopus 로고    scopus 로고
    • A weighted fuzzy classifier and its application to image processing tasks
    • Nakashima T, Schaefer G, Yokota Y, Ishibuchi H (2007) A weighted fuzzy classifier and its application to image processing tasks. Fuzzy Sets Syst 158: 284-294.
    • (2007) Fuzzy Sets Syst , vol.158 , pp. 284-294
    • Nakashima, T.1    Schaefer, G.2    Yokota, Y.3    Ishibuchi, H.4
  • 48
    • 33751186914 scopus 로고    scopus 로고
    • Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning
    • Ishibuchi H, Nojima Y (2007) Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning. Int J Approx Reason 44(1): 4-31.
    • (2007) Int J Approx Reason , vol.44 , Issue.1 , pp. 4-31
    • Ishibuchi, H.1    Nojima, Y.2
  • 49
    • 0035426682 scopus 로고    scopus 로고
    • Three-objective genetics-based machine learning for linguistic rule extraction
    • Ishibuchi H, Nakashima T, Murata T (2001) Three-objective genetics-based machine learning for linguistic rule extraction. Inf Sci 136: 109-133.
    • (2001) Inf Sci , vol.136 , pp. 109-133
    • Ishibuchi, H.1    Nakashima, T.2    Murata, T.3
  • 50
    • 0000719509 scopus 로고    scopus 로고
    • Voting in fuzzy rule-based systems for pattern classification problems
    • Ishibuchi H, Nakashima T, Morisawa T (1999) Voting in fuzzy rule-based systems for pattern classification problems. Fuzzy Sets Syst 103: 223-238.
    • (1999) Fuzzy Sets Syst , vol.103 , pp. 223-238
    • Ishibuchi, H.1    Nakashima, T.2    Morisawa, T.3
  • 51
    • 38649114684 scopus 로고    scopus 로고
    • Designing of classifiers based on immune principles and fuzzy rules
    • Lei Z, Ren-hou L (2008) Designing of classifiers based on immune principles and fuzzy rules. Inf Sci 178: 1836-1847.
    • (2008) Inf Sci , vol.178 , pp. 1836-1847
    • Lei, Z.1    Ren-Hou, L.2
  • 52
    • 38349177924 scopus 로고    scopus 로고
    • Data mining with a simulated annealing based fuzzy classification system
    • Mohamadi H, Habibi J, Abadeh MS, Saadi H (2008) Data mining with a simulated annealing based fuzzy classification system. Pattern Recognit 41(5): 1824-1833.
    • (2008) Pattern Recognit , vol.41 , Issue.5 , pp. 1824-1833
    • Mohamadi, H.1    Habibi, J.2    Abadeh, M.S.3    Saadi, H.4
  • 53
    • 0001920729 scopus 로고
    • Similarity metric learning for a variable-kernel classifier
    • Lowe DG (1995) Similarity metric learning for a variable-kernel classifier. Neural Comput 7: 72-85.
    • (1995) Neural Comput , vol.7 , pp. 72-85
    • Lowe, D.G.1
  • 54
    • 0016127071 scopus 로고
    • Finding prototypes for nearest neighbour classifiers
    • Chang CL (1974) Finding prototypes for nearest neighbour classifiers. IEEE Trans Comput 23: 1179-1184.
    • (1974) IEEE Trans Comput , vol.23 , pp. 1179-1184
    • Chang, C.L.1
  • 55
    • 0016969272 scopus 로고
    • An experiment with the edited nearest-neighbour rule
    • Tomek I (1976) An experiment with the edited nearest-neighbour rule. IEEE Trans Syst Man Cybern 6: 448-452.
    • (1976) IEEE Trans Syst Man Cybern , vol.6 , pp. 448-452
    • Tomek, I.1
  • 56
  • 57
    • 0038792231 scopus 로고    scopus 로고
    • Learning an adaptive dissimilarity measure for nearest neighbour classification
    • Verikas A, Bacauskiene M, Malmqvist K (2003) Learning an adaptive dissimilarity measure for nearest neighbour classification. Neural Comput Appl 11(3-4): 203-209.
    • (2003) Neural Comput Appl , vol.11 , Issue.3-4 , pp. 203-209
    • Verikas, A.1    Bacauskiene, M.2    Malmqvist, K.3
  • 58
    • 0016451032 scopus 로고
    • An experiment in linguistic synthesis with a fuzzy logic controller
    • Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man-Mach Stud 7(1): 1-12.
    • (1975) Int J Man-Mach Stud , vol.7 , Issue.1 , pp. 1-12
    • Mamdani, E.H.1    Assilian, S.2
  • 59
    • 84941531642 scopus 로고
    • A new approach to fuzzy-neural system modeling
    • Lin Y, Cunningham GA (1995) A new approach to fuzzy-neural system modeling. IEEE Trans Fuzzy Syst 3(2): 190-198.
    • (1995) IEEE Trans Fuzzy Syst , vol.3 , Issue.2 , pp. 190-198
    • Lin, Y.1    Cunningham, G.A.2
  • 60
    • 33947267482 scopus 로고    scopus 로고
    • Weighting fuzzy classification rules using receiver operating characteristics (roc) analysis
    • Zolghadri MJ, Mansoori EG (2007) Weighting fuzzy classification rules using receiver operating characteristics (roc) analysis. Inf Sci 177: 2296-2307.
    • (2007) Inf Sci , vol.177 , pp. 2296-2307
    • Zolghadri, M.J.1    Mansoori, E.G.2
  • 61
    • 10844255636 scopus 로고    scopus 로고
    • The evolving tree-a novel self-organizing network for data analysis
    • Pakkanen J, Iivarinen J, Oja E (2004) The evolving tree-a novel self-organizing network for data analysis. Neural Process Lett 20: 199-211.
    • (2004) Neural Process Lett , vol.20 , pp. 199-211
    • Pakkanen, J.1    Iivarinen, J.2    Oja, E.3
  • 63
    • 52449084807 scopus 로고    scopus 로고
    • An efficient technique to detect visual defects in particleboards
    • Guzaitis J, Verikas A (2008) An efficient technique to detect visual defects in particleboards. Informatica 19(3): 363-376.
    • (2008) Informatica , vol.19 , Issue.3 , pp. 363-376
    • Guzaitis, J.1    Verikas, A.2
  • 64
    • 0030111094 scopus 로고    scopus 로고
    • lvq combined with simulated annealing for optimal design of large-set reference patterns
    • Song HH, Lee SW (1996) lvq combined with simulated annealing for optimal design of large-set reference patterns. Neural Netw 9(2): 329-336.
    • (1996) Neural Netw , vol.9 , Issue.2 , pp. 329-336
    • Song, H.H.1    Lee, S.W.2
  • 67
    • 1842587806 scopus 로고    scopus 로고
    • Design of accurate classifiers with a compact fuzzy-rule base using an evolutionary scatter partition of feature space
    • Ho SY, Chen HM, Ho SJ (2004) Design of accurate classifiers with a compact fuzzy-rule base using an evolutionary scatter partition of feature space. IEEE Trans Syst Man Cybern Part B 34(2): 1031-1043.
    • (2004) IEEE Trans Syst Man Cybern Part B , vol.34 , Issue.2 , pp. 1031-1043
    • Ho, S.Y.1    Chen, H.M.2    Ho, S.J.3


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