TY - JOUR
T1 - Undersampling instance selection for hybrid and incomplete imbalanced data
AU - Camacho-Nieto, Oscar
AU - Yáñez-Márquez, Cornelio
AU - Villuendas-Rey, Yenny
N1 - Publisher Copyright:
© 2020, IICM. All rights reserved.
PY - 2020
Y1 - 2020
N2 - This paper proposes a novel undersampling method, for dealing with imbalanced datasets. The proposal is based on a novel instance importance measure (also introduced in this paper), and is able to balance hybrid and incomplete data. The numerical experiments carried out show the proposed undersampling algorithm outperforms others algorithms of the state of art, in well-known imbalanced datasets.
AB - This paper proposes a novel undersampling method, for dealing with imbalanced datasets. The proposal is based on a novel instance importance measure (also introduced in this paper), and is able to balance hybrid and incomplete data. The numerical experiments carried out show the proposed undersampling algorithm outperforms others algorithms of the state of art, in well-known imbalanced datasets.
KW - Hybrid and incomplete data
KW - Imbalanced data
KW - Undersampling
UR - http://www.scopus.com/inward/record.url?scp=85090620428&partnerID=8YFLogxK
M3 - Artículo
AN - SCOPUS:85090620428
SN - 0948-695X
VL - 26
SP - 698
EP - 719
JO - Journal of Universal Computer Science
JF - Journal of Universal Computer Science
IS - 6
ER -