Name
Abstract | ||
|---|---|---|
Some of the real-world problems are represented with just one label but many of today's issues are currently being defined with multiple labels. This second group is important because multi-label classes provide a more global picture of the problem. From the study of the characteristics of the most influential systems in this area, MlKnn and RAkEL, we can observe that the main drawback of these specific systems is the time required. Therefore, the aim of the current paper is to develop a more efficient system in terms of computation without incurring accuracy loss. To meet this objective we propose MlCBR, a system for multi-label classification based on Case-Based Reasoning. The results obtained highlight the strong performance of our algorithm in comparison with previous benchmark methods in terms of accuracy rates and computational time reduction. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.eswa.2013.05.004 | Expert Syst. Appl. |
Keywords | Field | DocType |
computational time reduction,multi-label class,current paper,accuracy rate,efficient system,analog reasoning,incurring accuracy loss,specific system,multi-label classification,case-based reasoning,influential system,case based reasoning,classification | Drawback,Data mining,Computer science,Multi-label classification,Artificial intelligence,Case-based reasoning,Machine learning,Computation | Journal |
Volume | Issue | ISSN |
40 | 15 | 0957-4174 |
Citations | PageRank | References |
3 | 0.37 | 17 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruben Nicolas | 1 | 4 | 1.74 |
| Andreu Sancho-Asensio | 2 | 28 | 3.35 |
| Elisabet Golobardes | 3 | 206 | 20.16 |
| Albert Fornells | 4 | 118 | 9.27 |
| Albert Orriols-Puig | 5 | 511 | 25.91 |