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

Authors:
Eva Chung-Chiung Yen
Affiliations:
National Central University, Zhongli, Taiwan
Venue:
Artificial Intelligence Review
Year:
2008

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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.