relation: https://eprints.uet.vnu.edu.vn/eprints/id/eprint/1939/
title: FRFE: Fast Recursive Feature Elimination for Credit Scoring
creator: Ha, Van Sang
creator: Nguyen, Ha Nam
subject: Information Technology (IT)
description: Abstract Credit scoring is one of the most important issues in financial decision-making. The use of data mining techniques to build models for credit scoring has been a hot topic in recent years. Classification problems often have a large number of features, but not all of them are useful for classification. Irrelevant and redundant features in credit data may even reduce the classification accuracy. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, reduce the running time, and  ...
date: 2016-10
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Ha, Van Sang and Nguyen, Ha Nam  (2016) FRFE: Fast Recursive Feature Elimination for Credit Scoring.  In: International Conference on Nature of Computation and Communication, 2016.     
relation: http://link.springer.com/chapter/10.1007/978-3-319-46909-6_13