scholarly journals An innovative feature selection method for support vector machines and its test on the estimation of the credit risk of default

2018 ◽  
Vol 37 (3) ◽  
pp. 404-427 ◽  
Author(s):  
Eduard Sariev ◽  
Guido Germano
Keyword(s):  
Feature Selection ◽  
Support Vector Machines ◽  
Credit Risk ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Vector Machines

scholarly journals Burned Area Mapping Using Support Vector Machines and the FuzCoC Feature Selection Method on VHR IKONOS Imagery

2014 ◽  
Vol 6 (12) ◽  
pp. 12005-12036 ◽  
Author(s):  
Eleni Dragozi ◽  
Ioannis Gitas ◽  
Dimitris Stavrakoudis ◽  
John Theocharis
Keyword(s):  
Feature Selection ◽  
Support Vector Machines ◽  
Feature Selection Method ◽  
Selection Method ◽  
Burned Area ◽  
Support Vector ◽  
Vector Machines ◽  
Burned Area Mapping

SVM-Based Credit Rating and Feature Selection

2014 ◽  
Vol 618 ◽  
pp. 573-577 ◽  
Author(s):  
Yu Qiang Qin ◽  
Yu Dong Qi ◽  
Hui Ying
Keyword(s):  
Logistic Regression ◽  
Feature Selection ◽  
Financial Institutions ◽  
Credit Card ◽  
Credit Rating ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Vector Machines ◽  
Reference Agency

The assessment of risk of default on credit is important for financial institutions. Logistic regression and discriminant analysis are techniques traditionally used in credit rating for determining likelihood to default based on consumer application and credit reference agency data. We test support vector machines (SVM) against these traditional methods on a large credit card database. We find that they are competitive and can be used as the basis of a feature selection method to discover those features that are most significant in determining risk of default.

scholarly journals A Jackknife and Voting Classifier Approach to Feature Selection and Classification

2011 ◽  
Vol 10 ◽  
pp. CIN.S7111 ◽  
Author(s):  
Sandra L. Taylor ◽  
Kyoungmi Kim
Keyword(s):  
Feature Selection ◽  
Support Vector Machines ◽  
Random Forest ◽  
Clinical Setting ◽  
Feature Selection Method ◽  
Classification Rule ◽  
Support Vector ◽  
Technological Advances ◽  
Vector Machines ◽  
Jackknife Procedure

With technological advances now allowing measurement of thousands of genes, proteins and metabolites, researchers are using this information to develop diagnostic and prognostic tests and discern the biological pathways underlying diseases. Often, an investigator's objective is to develop a classification rule to predict group membership of unknown samples based on a small set of features and that could ultimately be used in a clinical setting. While common classification methods such as random forest and support vector machines are effective at separating groups, they do not directly translate into a clinically-applicable classification rule based on a small number of features. We present a simple feature selection and classification method for biomarker detection that is intuitively understandable and can be directly extended for application to a clinical setting. We first use a jackknife procedure to identify important features and then, for classification, we use voting classifiers which are simple and easy to implement. We compared our method to random forest and support vector machines using three benchmark cancer ‘omics datasets with different characteristics. We found our jackknife procedure and voting classifier to perform comparably to these two methods in terms of accuracy. Further, the jackknife procedure yielded stable feature sets. Voting classifiers in combination with a robust feature selection method such as our jackknife procedure offer an effective, simple and intuitive approach to feature selection and classification with a clear extension to clinical applications.

Sign in / Sign up

Export Citation Format

Share Document