Paper Info
Title
Selection of Support Vector Machines based classifiers for credit risk domain
Abstract
We propose an approach combining feature selection, classification and sliding window testing.We propose a technique based on linear Support Vector Machines and particle swarm optimization.Proposed technique is also focused on imbalanced learning.Proposed technique can be useful to identify \"risky\" companies which are represented as \"minority\" in imbalanced datasets.Experiment is performed on real world financial dataset from SEC EDGAR database. This paper describes an approach for credit risk evaluation based on linear Support Vector Machines classifiers, combined with external evaluation and sliding window testing, with focus on application on larger datasets. It presents a technique for optimal linear SVM classifier selection based on particle swarm optimization technique, providing significant amount of focus on imbalanced learning issue. It is compared to other classifiers in terms of accuracy and identification of each class. Experimental classification performance results, obtained using real world financial dataset from SEC EDGAR database, lead to conclusion that proposed technique is capable to produce results, comparable to other classifiers, such as logistic regression and RBF network, and thus be can be an appealing option for future development of real credit risk evaluation models.
Year
DOI
Venue
2015
10.1016/j.eswa.2014.12.001
Expert Syst. Appl.
Keywords
Field
DocType
particle swarm optimization,credit risk,classification,support vector machines,svm
Particle swarm optimization,Data mining,Sliding window protocol,Feature selection,Random subspace method,Computer science,Support vector machine,Artificial intelligence,Classifier (linguistics),Logistic regression,Machine learning,Credit risk
Journal
Volume
Issue
ISSN
42
6
0957-4174
Citations 
PageRank 
References 
16
0.75
41
Authors
2
Name
Order
Citations
PageRank
Paulius Danenas1355.07
Gintautas Garsva2414.95