Open Access. Powered by Scholars. Published by Universities.®

Business Commons

Open Access. Powered by Scholars. Published by Universities.®

Management Information Systems

PDF

California State University, San Bernardino

2014

Method

Articles 1 - 2 of 2

Full-Text Articles in Business

Entropy Based Feature Selection For Multi-Relational Naïve Bayesian Classifier, Vimalkumar B. Vaghela, Kalpesh H. Vandra, Nilesh K. Modi Jan 2014

Entropy Based Feature Selection For Multi-Relational Naïve Bayesian Classifier, Vimalkumar B. Vaghela, Kalpesh H. Vandra, Nilesh K. Modi

Journal of International Technology and Information Management

Current industries data’s are stored in relation structures. In usual approach to mine these data, we often use to join several relations to form a single relation using foreign key links, which is known as flatten. Flatten may cause troubles such as time consuming, data redundancy and statistical skew on data. Hence, the critical issues arise that how to mine data directly on numerous relations. The solution of the given issue is the approach called multi-relational data mining (MRDM). Other issues are irrelevant or redundant attributes in a relation may not make contribution to classification accuracy. Thus, feature selection is …


The Classification Performance Of Multiple Methods And Datasets: Cases From The Loan Credit Scoring Domain, Jozef Zurada, Niki Kunene, Jian Guan Jan 2014

The Classification Performance Of Multiple Methods And Datasets: Cases From The Loan Credit Scoring Domain, Jozef Zurada, Niki Kunene, Jian Guan

Journal of International Technology and Information Management

Decisions to extend credit to potential customers are complex, risky and even potentially catastrophic for the credit granting institution and the broader economy as underscored by credit failures in the late 2000s. Thus, the ability to accurately assess the likelihood of default is an important issue. In this paper the authors contrast the classification accuracy of multiple computational intelligence methods using five datasets obtained from five different decision contexts in the real world. The methods considered are: logistic regression (LR), neural network (NN), radial basis function neural network (RBFNN), support vector machine (SVM), k-nearest neighbor (kNN), and decision tree (DT). …