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 nonrigid inference mechanisms, easier understanding and ability to model functions of arbitrary complexity. [ABSTRACT FROM AUTHOR]