TY - JOUR
T1 - NACOD
T2 - A naïve associative classifier for online data
AU - Villuendas-Rey, Yenny
AU - Hernández-Castaño, Javier A.
AU - Camacho-Nieto, Oscar
AU - Yáñez-Márquez, Cornelio
AU - López-Yañez, Itzamá
N1 - Publisher Copyright:
© 2019 Oxford University Press. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Analyzing data in real time constitutes a challenge nowadays, due to the constant generation of data from different sources. To deal to such streams of data, in this paper we propose a novel decision-making algorithm within the associative approach. The proposed algorithm, named Naïve Associative Classifier for Online Data (NACOD), is able to deal with hybrid as well as with incomplete data. In addition, NACOD is transparent and transportable, which makes it a very useful decision-maker in environments that require such properties. The numerical experiments carried out show the effectiveness of NACOD.
AB - Analyzing data in real time constitutes a challenge nowadays, due to the constant generation of data from different sources. To deal to such streams of data, in this paper we propose a novel decision-making algorithm within the associative approach. The proposed algorithm, named Naïve Associative Classifier for Online Data (NACOD), is able to deal with hybrid as well as with incomplete data. In addition, NACOD is transparent and transportable, which makes it a very useful decision-maker in environments that require such properties. The numerical experiments carried out show the effectiveness of NACOD.
KW - Decision-making
KW - Hybrid and incomplete data
KW - Naïve associative classifier
KW - Online learning
UR - http://www.scopus.com/inward/record.url?scp=85075206624&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2936366
DO - 10.1109/ACCESS.2019.2936366
M3 - Artículo
AN - SCOPUS:85075206624
SN - 2169-3536
VL - 7
SP - 117761
EP - 117767
JO - IEEE Access
JF - IEEE Access
M1 - 8805380
ER -