Name
Title | ||
|---|---|---|
Improving The Performance Of An Associative Classifier By Gamma Rough Sets Based Instance Selection |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1142/S0218001418600091 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
Gamma rough sets, instance selection, associative classifiers, lazy learners | k-nearest neighbors algorithm,Management information systems,Pattern recognition,Associative classifier,Rough set,Instance selection,Artificial intelligence,Classifier (linguistics),Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
32 | 1 | 0218-0014 |
Citations | PageRank | References |
2 | 0.36 | 15 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jarvin A. Antón Vargas | 1 | 2 | 0.36 |
| Yenny Villuendas-rey | 2 | 46 | 14.38 |
| Cornelio Yanez Marquez | 3 | 17 | 2.99 |
| Itzamá López-Yáñez | 4 | 78 | 11.76 |
| Oscar Camacho Nieto | 5 | 65 | 14.93 |