fuzzy c-regression models (FCRM)
Comparison between multiple linear regression and fuzzy C regression models towards scale of health in ICU
... linear regression (MLR) model is well-known in analyzing linear ...the fuzzy c-regression models (FCRM) are being widely used in analyzing the nonlinear ...FCRM models are tested ...
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Adjustment of an intensive care unit (ICU) data in fuzzy C regression models
... and fuzzy c-mean (FCM) ...using fuzzy c- means (FCM) model. The models used in this research are fuzzy c-regression models ...FCRM models was carried ...
10
On the use of fuzzy c regression truncated models for health indicator in intensive care unit
... The models/technique discussed are multiple linear regression MLR model, analytical hierarchy process AHP technique, fuzzy c-means FCM model, fuzzy c-regression models FCRM, fuzzy linear[r] ...
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On the use of fuzzy C-regression truncated models for health indicator in intensive care unit
... logistic regression model. The fuzzy models are still not a common method used in the ...basic fuzzy rule which is given by physician to determine the medical decision made in the ...25 ...
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Alternative Fuzzy Switching Regression
... a c=2 simple switching regression model with two parallel ...estimated regression models of EM, FCR and AFCR are shown in ...a regression model with two crossed lines and two outlier ...
5
A technique of fuzzy c mean in multiple linear regression model toward paddy yield
... Multiple regression models is a linear regression model with two or more predictors and one ...also regression models with two or more response ...These models are usually called ...
8
A new hybrid of fuzzy c means method and fuzzy linear regression model in predicting manufacturing income
... Thus, fuzzy structure system is ...using fuzzy c-means (FCM) method, to apply existing methods such as multiple linear regression (MLR) and fuzzy linear regression (FLR) as ...
6
Impact on Brain due to Alcoholism using Improved Fuzzy C Regression based Alcohol Detection
... as regression, clustering, association rule mining and ...problem, Fuzzy C-Regression (FCR) model has been proposed that helps to predict the non-linearity distribution of GMV in the brain ...
9
Hierarchical Alpha-cut Fuzzy C-means, Fuzzy ARTMAP and Cox Regression Model for Customer Churn Prediction
... including 495 churners and 2,655 non-churners, from September 2008 to August 2009. In addition, the subscribers have to be mature customers who are with the mobile operator for at least 2 months. Churn is then calculated ...
10
Application of fuzzy linear regression models for predicting tumor size of colorectal cancer in Malaysia’s hospital
... new fuzzy approach for the automatic segmentation of normal and pathological brain magnetic resonance imaging ...is fuzzy c-means (FCM) ...well-known fuzzy and non-fuzzy ...
45
Application of fuzzy and possibilistic c-means clustering models in blind speaker clusterin
... cognitive load during speaking [10-12]. Several of these tasks attempt to detect phenomena which vary from speaker to speaker. Therefore, in these tasks, if we could identify which utterances (recordings) belong to the ...
16
A Comparison of Classification/Regression Trees and Logistic Regression in Failure Models
... relevant models specifically made for SMEswas developed by (Altman, & Sabato, ...new models which consider other financial variables and use traditional logistic ...new models outperform the ...
6
Fuzzy ∗ homomorphisms and fuzzy ∗ derivations in induced fuzzy C∗ algebras
... a fuzzy norm in such a manner that the corresponding fuzzy metric is of Karmosil and Michalek type ...a fuzzy norm into a family of crisp norms and investigated some properties of fuzzy normed ...
10
Fuzzy Regression Model and Its Application: A Review
... proposed fuzzy linear regression model with crisp input and fuzzy output described by Tanaka was considered ...modified fuzzy least square method for the ...conventional fuzzy least ...
8
Least Squares Support Vector Fuzzy Regression
... vector fuzzy regression model (LS_SVFR) is proposed to estimate uncertain and imprecise data by applying the fuzzy sets principle in weight ...vector fuzzy regression ...
6
Fuzzy C-means++: Fuzzy C-means with effective seeding initialization
... proposed Fuzzy C-means++ scheme (Algorithm 2) is to choose points that are spread out in the data set as representatives and update the membership matrix accordingly before commenc- ing Steps ...the ...
8
Diagnostic in Poisson Regression Models
... the following. We briefly present in section 2 the estimation of the Poisson regression parameters for both deleted and undeleted observation. In section 3, we introduced the deviance and Pearson χ 2 criteria to ...
9
Inference for Approximating Regression Models
... squares regression at the population ...that regression defines, rather than the slopes of an assumed linear model, become the population quantities of interest, and they can be estimated ...population ...
98
Comparison of Fuzzy C-Means, Fuzzy Kernel C-Means, and Fuzzy Kernel Robust C-Means to Classify Thalassemia Data
... by fuzzy c-means, fuzzy robust c-means, and fuzzy kernel robust ...the fuzzy kernel robust c-means is the best among the other three ...
6
Semiparametric Regression Models for Interacting Covariates.
... nonparametric regression methods based on kernel machine regression (KMR) have become very popular in association testing problems because of their ability to incorporate complex interactions (Liu et ...
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