Name
Abstract | ||
|---|---|---|
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 Naive 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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2936366 | IEEE ACCESS |
Keywords | DocType | Volume |
Decision-making,online learning,hybrid and incomplete data,naive associative classifier | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 1 | 0.35 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yenny Villuendas-rey | 1 | 46 | 14.38 |
| Javier A. Hernandez-Castano | 2 | 1 | 0.35 |
| Oscar Camacho Nieto | 3 | 65 | 14.93 |
| Cornelio Yanez Marquez | 4 | 17 | 2.99 |
| Itzama Lopez-Yanez | 5 | 1 | 0.35 |