eprintid: 1939
rev_number: 9
eprint_status: archive
userid: 300
dir: disk0/00/00/19/39
datestamp: 2016-11-24 09:20:05
lastmod: 2016-11-24 09:20:05
status_changed: 2016-11-24 09:20:05
type: conference_item
metadata_visibility: show
creators_name: Ha, Van Sang
creators_name: Nguyen, Ha Nam
creators_id: namnh@vnu.edu.vn
title: FRFE: Fast Recursive Feature Elimination for Credit Scoring
ispublished: pub
subjects: IT
divisions: fac_fit
abstract: 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
date_type: completed
official_url: http://link.springer.com/chapter/10.1007/978-3-319-46909-6_13
full_text_status: none
pres_type: paper
pagerange: 133-142
event_title: International Conference on Nature of Computation and Communication
event_dates: 2016
event_type: conference
refereed: TRUE
related_url_type: author
citation:   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.