FUZZY LOGISTIC REGRESSION BASED ON LEAST SQUARE APPROACH AND TRAPEZOIDAL MEMBERSHIP FUNCTION

Logistic regression is a non-linear modification of the linear regression. The purpose of the logistic regression analysis is to measure the effects of multiple explanatory variables which can be continuous and response variable is categorical. In real life there are situations which we deal with information that is vague in nature and there are cases that are not explained precisely. In this regard, we have used the concept of possiblistic odds and fuzzy approach. Fuzzy logic deals with linguistic uncertainties and extracting valuable information from linguistic terms. In our study, we have developed fuzzy possiblistic logistic model with trapezoidal membership function and fuzzy possiblistic logistic model is a tool that help us to deal with imprecise observations. Comparison fuzzy logistic regression model with classical logistic regression has been done by goodness of fit criteria on real life as an example.