SELECTION OF DECISION BOUNDARIES FOR LOGISTIC REGRESSION
نویسندگان
چکیده
منابع مشابه
Selection of classification boundaries using the logistic regression
We propose the method for selecting classification boundaries for the logistic regression model, by applying the sparse regularization. We can investigate which combination of classification boundaries are truly necessary for the multinomial logistic model by encouraging some of coefficient parameters themselves or their differences toward exactly zeros. The model is estimated by the maximum pe...
متن کاملVariable Selection for Multivariate Logistic Regression Models
In this paper, we use multivariate logistic regression models to incorporate correlation among binary response data. Our objective is to develop a variable subset selection procedure to identify important covariates in predicting correlated binary responses using a Bayesian approach. In order to incorporate available prior information, we propose a class of informative prior distributions on th...
متن کاملEnsemble Logistic Regression for Feature Selection
This paper describes a novel feature selection algorithm embedded into logistic regression. It specifically addresses high dimensional data with few observations, which are commonly found in the biomedical domain such as microarray data. The overall objective is to optimize the predictive performance of a classifier while favoring also sparse and stable models. Feature relevance is first estima...
متن کاملSample size determination for logistic regression
The problem of sample size estimation is important in medical applications, especially in cases of expensive measurements of immune biomarkers. This paper describes the problem of logistic regression analysis with the sample size determination algorithms, namely the methods of univariate statistics, logistics regression, cross-validation and Bayesian inference. The authors, treating the regr...
متن کاملA Purposeful Selection of Variables Macro for Logistic Regression
The main problem in any model-building situation is to choose from a large set of covariates those that should be included in the “best” model. A decision to keep a variable in the model might be based on the clinical or statistical significance. There are several variable selection algorithms embedded in SAS PROC LOGISTIC. Those methods are mechanical and as such carry some limitations. Hosmer...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Bulletin of informatics and cybernetics
سال: 2015
ISSN: 0286-522X
DOI: 10.5109/1909526