TY - JOUR
T1 - Improving the Performance of an Associative Classifier by Gamma Rough Sets Based Instance Selection
AU - Antón-Vargas, Jarvin A.
AU - Villuendas-Rey, Yenny
AU - Yáñez-Márquez, Cornelio
AU - López-Yáñez, Itzamá
AU - Camacho-Nieto, Oscar
N1 - Publisher Copyright:
© 2018 World Scientific Publishing Company.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - This paper introduces the Gamma Rough Sets for management information systems where the universe objects are represented by continuous attributes and are connected by similarity relations. Some properties of such sets are demonstrated in this paper. In addition, Gamma Rough Sets are used to improve the Gamma associative classifier, by selecting instances. The results indicate that the selection of instances significantly reduces the computational cost of the Gamma classifier without affecting its effectiveness. The results also suggest that the selection of instances using Gamma Rough Sets favors other lazy learners, such as Nearest Neighbor and ALVOT.
AB - This paper introduces the Gamma Rough Sets for management information systems where the universe objects are represented by continuous attributes and are connected by similarity relations. Some properties of such sets are demonstrated in this paper. In addition, Gamma Rough Sets are used to improve the Gamma associative classifier, by selecting instances. The results indicate that the selection of instances significantly reduces the computational cost of the Gamma classifier without affecting its effectiveness. The results also suggest that the selection of instances using Gamma Rough Sets favors other lazy learners, such as Nearest Neighbor and ALVOT.
KW - Gamma rough sets
KW - associative classifiers
KW - instance selection
KW - lazy learners
UR - http://www.scopus.com/inward/record.url?scp=85026423646&partnerID=8YFLogxK
U2 - 10.1142/S0218001418600091
DO - 10.1142/S0218001418600091
M3 - Artículo
SN - 0218-0014
VL - 32
JO - International Journal of Pattern Recognition and Artificial Intelligence
JF - International Journal of Pattern Recognition and Artificial Intelligence
IS - 1
M1 - 1860009
ER -