Logistic and SVM Credit Score Models Based on Lasso Variable Selection
نویسندگان
چکیده
منابع مشابه
Variable 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 کاملCredit Scoring Models with AUC Maximization Based on Weighted SVM
Credit scoring models are very important tools for financial institutions to make credit granting decisions. In the last few decades, many quantitative methods have been used for the development of credit scoring models with focus on maximizing classification accuracy. This paper proposes the credit scoring models with the area under receiver operating characteristics curve (AUC) maximization b...
متن کاملVariable Selection Using SVM-based Criteria
We propose new methods to evaluate variable subset relevance with a view to variable selection. Relevance criteria are derived from Support Vector Machines and are based on weight vector ‖w‖2 or generalization error bounds sensitivity with respect to a variable. Experiments on linear and non-linear toy problems and real-world datasets have been carried out to assess the effectiveness of these c...
متن کاملLarge Unbalanced Credit Scoring Using Lasso-Logistic Regression Ensemble
Recently, various ensemble learning methods with different base classifiers have been proposed for credit scoring problems. However, for various reasons, there has been little research using logistic regression as the base classifier. In this paper, given large unbalanced data, we consider the plausibility of ensemble learning using regularized logistic regression as the base classifier to deal...
متن کاملA variable selection method based on Tabu search for logistic regression models
A Tabu Search method to select variables that are subsequently used in Logistic Regression Models is proposed and analysed. The aim is to find from among a set of m variables a smaller subset which enables an efficient classification of cases. Reducing dimensionality has some advantages such as reducing the costs of data acquisition, better understanding of the final classification model, and a...
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: Journal of Applied Mathematics and Physics
سال: 2019
ISSN: 2327-4352,2327-4379
DOI: 10.4236/jamp.2019.75076