TY - GEN
T1 - Reducing Dimensionality of Variables for a Classification Problem
AU - Shiguihara-Juarez, Pedro
AU - Murrugarra-Llerena, Nils
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Fraud detection can be considered as a classification task since we can use datasets with labelled instances as fraud cases and legal cases. Although, many classifiers were applied to this problem, the data pre-processing related to the reduction of values of each variable is an uncommon approach. We explore a method to reduce the cardinality of the variables in a dataset of fraud transaction to identify improvement in this classification problem. Our best result indicated an improvement of + 31.8% in terms of F1-measure when we reduce the cardinality to detect fraud cases.
AB - Fraud detection can be considered as a classification task since we can use datasets with labelled instances as fraud cases and legal cases. Although, many classifiers were applied to this problem, the data pre-processing related to the reduction of values of each variable is an uncommon approach. We explore a method to reduce the cardinality of the variables in a dataset of fraud transaction to identify improvement in this classification problem. Our best result indicated an improvement of + 31.8% in terms of F1-measure when we reduce the cardinality to detect fraud cases.
KW - Feature Engineering
KW - Pre-processing of attributes
KW - Reduction of Cardinality of Variables
UR - http://www.scopus.com/inward/record.url?scp=85082395783&partnerID=8YFLogxK
U2 - 10.1109/SHIRCON48091.2019.9024863
DO - 10.1109/SHIRCON48091.2019.9024863
M3 - Contribución a la conferencia
AN - SCOPUS:85082395783
T3 - SHIRCON 2019 - 2019 IEEE Sciences and Humanities International Research Conference
BT - SHIRCON 2019 - 2019 IEEE Sciences and Humanities International Research Conference
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 November 2019 through 15 November 2019
ER -