This paper proposes an adaptive technique in the prediction of dichotomous response variable by combining fuzzy concept with statistical logistic regression. The model was tested on cancer dataset in predicting cancer susceptibility. In this paper we will present the development, evaluation and validation of the proposed model based on the experiment carried out. Explanatory power of the adapti...

This paper proposes an adaptive technique in the prediction of dichotomous response variable by combining fuzzy concept with statistical logistic regression. The model was tested on cancer dataset in predicting cancer susceptibility. In this paper we will present the development, evaluation and validation of the proposed model based on the experiment carried out. Explanatory power of the adapti...

Social judgment data was analyzed using fuzzy least squares regression analysis based on the extension principle. The proposed analysis is new fuzzy least squares regression analysis in which input data, output data, and coefficients are represented by L-R fuzzy numbers. To evaluate data fitness, we propose a fuzzy version of a squared multiple correlation (R) and conducted an experiment to det...

The terminal nodes of a binary tree classifier represent discrete classes to be recognized. In this paper the classes are considered to be fuzzy sets in which a specific sample can belong to more than one class with different degrees of membership. The terminal nodes is this case will contain information about the degrees to which test samples belong to particular classes. This will allow the d...

This communication is concerned with the problem of supervised classification of fuzzy data obtained from a random experiment. The data generation process is modelled through fuzzy random variables which, from a formal point of view, can be identified with a kind of functional random element. We propose to adapt one of the most versatile discriminant approaches in the context of functional data...

Dichotomization of the outcome by a single cut-off point is an important part of various medical studies. Usually the relationship between the resulted dichotomized dependent variable and explanatory variables is analyzed with linear regression, probit regression or logistic regression. However, in many real-life situations, a certain cut-off point dividing the outcome into two groups is unknow...

A generalized simple linear regression statistical/probabilistic model in which both input and output data can be fuzzy subsets of Rp is dealt with. The regression model is based on a fuzzy-arithmetic approach and it considers the possibility of fuzzy-valued random errors. Specifically, the least-squares estimation problem in terms of a versatile metric is addressed. The solutions are establish...

background: noise prediction is considered to be the best method for evaluating cost-preventative noise controls in industrial workrooms. one of the most important issues is the development of accurate models for analysis of the complex relationships among acoustic features affecting noise level in workrooms. in this study, advanced fuzzy approaches were employed to develop relatively accurate ...