Using Phi Coefficient to Interpret Results Obtained by InterCriteria Analysis
The authors propose an algorithm for assessment of the estimates of “correspondence” and “opposition” obtained by InterCriteria Analysis (ICA) in the form of intuitionistic fuzzy vector pairs. For this aim the modified Pearson coefficient of Karl Pearson, called \(\varphi \) coefficient (“mean square contingency coefficient”). The algorithm is applied on real data from neurosurgery. The statistical significance of the relations between the considered criteria is verified by data found in literature. The authors believe this approach for data exploration may prove useful in many areas.
KeywordsInterCriteria analysis Pearson phi Correlation Intuitionistic fuzzy vector pairs
The first two authors are grateful for the support provided by Grant DFNI-I-02-5 “InterCriteria Analysis—A New Approach to Decision Making” of the Bulgarian National Science Fund.
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