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Ordinal preference and inter-rater pattern recognition: Hopfield neural network vs. measures of association

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Abstract

In decision analysis, if the criterion is an ordinal rather than a cardinal one, a preferential solution depends on the inter-rater agreement. The Kendall coefficient of concordance W, the Friedman ranks statistic F r , and the Page L statistic are often used to determine the association among M sets of rankings. However, they may get some anomalies because they all use the cardinal variable “variance” to judge the association. In order to correct the anomalies, we use the modified Hopfield neural network instead to determine the association among M sets of rankings. The results are not only to reduce the unidentified cases but also to solve the anomalies.

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References

  • Aitkenhead MJ, McDonald AJS (2003) A neural network face recognition system. Eng Appl Artif Intell 16: 167–176

    Article  Google Scholar 

  • Brause R (1995) Self-organized learning in multi-layer networks. Int J Artif Intell Tools 4: 433–451

    Article  Google Scholar 

  • Chou P (1989) The capacity of the Kanerva associative memory. IEEE Trans Inf Theory 35: 198–281

    Google Scholar 

  • Haralick RM, Shanmugam K, Dinstein I (1973) Texture features for image classification. IEEE Trans Syst Man Cybern 3: 610–621

    Article  Google Scholar 

  • Ho T, Hull J, Srihari S (1994) Decision combination in multiple classifier system. IEEE Trans Neural Netw 16: 66–75

    Google Scholar 

  • Hopfield J, Tank D (1985) Neural computations of decisions in optimization problems. Biol Cybern 51: 141–152

    MathSciNet  Google Scholar 

  • Huang J, Wechsler H (1999) Eye detection using optimal wavelet packets and radial basis functions (RBFs). Intern J Pattern Recognit Artif Intell 13: 1009–1026

    Article  Google Scholar 

  • Ikeda N, Watta P, Artiklar M, Hassoun MH (2001) A two-level Hamming network for high performance associative memory. Neural Netw 14: 1189–1200

    Article  Google Scholar 

  • Kittler J, Hatef M, Duin RPW, Matas J (1998) On combining classifiers. Intern J Pattern Recognit Artif Intell 20: 226–239

    Google Scholar 

  • Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33: 159–174

    Article  MATH  MathSciNet  Google Scholar 

  • Lawrence S, Giles CL, Tsoi AC, Back AD (1997) Face recognition: a convolutional neural network approach. IEEE Trans Neural Netw 8: 98–113

    Article  Google Scholar 

  • Legendre P (2000) Comparison of permutation methods for the partial correlation and partial mantel tests. J Stat Comput Simul 67: 37–73

    Article  MATH  MathSciNet  Google Scholar 

  • Legendre P (2005) Species associations: the Kendall coefficient of concordance revisited. J Agric Biol Environ Stat 10: 226–245

    Article  Google Scholar 

  • Lorenzo A, Blanche CA, Henson JF (2003) Concordance among extension workers, researchers, and professional arborists in rating landscape trees. J Ext 41: 165–176

    Google Scholar 

  • Manjunath B, Shekhar C, Chellappa R (1996) A new approach to image feature detection with applications. Pattern Recognit 31: 627–640

    Article  Google Scholar 

  • Rowley HA, Baluja S, kanade T (1996) Neural network-based face detection. IEEE Trans Pattern Anal Mach Intell 20: 23–38

    Article  Google Scholar 

  • Siegel S, Castellan NJ Jr (1988) Nonparametric statistics for the behavioral sciences, 2nd edn. McGraw-Hill, New York

    Google Scholar 

  • Smith KA, Abramson D, Duke D (2003) Hopfield neural networks for timetabling: formulations, methods, and comparative results. Comput Ind Eng 44: 283–305

    Article  Google Scholar 

  • Wang S (2005) Classification with incomplete survey data: a Hopfield neural network approach. Comp Oper Res 32: 2583–2594

    Article  MATH  Google Scholar 

  • Wang Z, Guerriero A, De Sario M (1996) Comparison of several approaches for the segmentation of texture images. Pattern Recognit Lett 17: 509–521

    Article  Google Scholar 

Download references

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Correspondence to Eva Chung-chiung Yen.

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Yen, E.Cc. Ordinal preference and inter-rater pattern recognition: Hopfield neural network vs. measures of association. Artif Intell Rev 30, 87 (2008). https://doi.org/10.1007/s10462-009-9118-5

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  • DOI: https://doi.org/10.1007/s10462-009-9118-5

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