Classifying with fuzzy chi-square test: The case of invasive species

Given that the Chi-Square Test imparts a binary correlation between the variables examined and it does not offer the exact degree of dependence or independence which is always a major issue, this research proposes an innovative method of yielding these values with precision and accuracy. More specifically, this paper introduces and applies the Fuzzy Chi-Square Test, which fuzzifies the Chi-Square Test’s p-value by employing linguistics like Low, Medium, High in order to incorporate the level of dependence or independence of the variables examined. In this way it renders non-rigid inference mechanisms, easier understanding and ability to model functions of arbitrary complexity.Given that the Chi-Square Test imparts a binary correlation between the variables examined and it does not offer the exact degree of dependence or independence which is always a major issue, this research proposes an innovative method of yielding these values with precision and accuracy. More specifically, this paper introduces and applies the Fuzzy Chi-Square Test, which fuzzifies the Chi-Square Test’s p-value by employing linguistics like Low, Medium, High in order to incorporate the level of dependence or independence of the variables examined. In this way it renders non-rigid inference mechanisms, easier understanding and ability to model functions of arbitrary complexity.

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