TY - JOUR
T1 - An extension of the gamma associative classifier for dealing with hybrid data
AU - Villuendas-Rey, Yenny
AU - Yanez-Marquez, Cornelio
AU - Anton-Vargas, Jarvin A.
AU - Lopez-Yanez, Itzama
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2019
Y1 - 2019
N2 - This paper extends the Gamma associative classifier, making it able to deal with hybrid and incomplete data. In addition, it also encompasses the gamma rough sets model for dealing with such data, introducing the extended gamma rough sets. Some properties of such sets are demonstrated in this paper. In turn, the novel extended gamma rough sets are used to improve the extended gamma associative classifier by selecting the instances. The results indicate that the selection of instances significantly improves the accuracy of the extended gamma associative classifier while reducing its computational cost.
AB - This paper extends the Gamma associative classifier, making it able to deal with hybrid and incomplete data. In addition, it also encompasses the gamma rough sets model for dealing with such data, introducing the extended gamma rough sets. Some properties of such sets are demonstrated in this paper. In turn, the novel extended gamma rough sets are used to improve the extended gamma associative classifier by selecting the instances. The results indicate that the selection of instances significantly improves the accuracy of the extended gamma associative classifier while reducing its computational cost.
KW - Associative classifiers
KW - hybrid data
KW - rough sets
UR - http://www.scopus.com/inward/record.url?scp=85066613611&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2916795
DO - 10.1109/ACCESS.2019.2916795
M3 - Artículo
AN - SCOPUS:85066613611
SN - 2169-3536
VL - 7
SP - 64198
EP - 64205
JO - IEEE Access
JF - IEEE Access
M1 - 8715514
ER -