Name
Abstract | ||
|---|---|---|
A case selection algorithm selects representative cases from a large data set for future case-based reasoning tasks. This paper proposes the SRS algorithm, based on similarity-based rough set theory, which selects a reasonable number of the representative cases while maintaining satisfactory classification accuracy. It also can handle noise and inconsistent data. Experimental results have confirmed the algorithm feasibility and the validity. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/FSKD.2008.13 | FSKD (5) |
Keywords | Field | DocType |
srs algorithm,large data,similarity rough set,algorithm feasibility,similarity-based rough set theory,representative case,future case-based reasoning task,case selection algorithm,inconsistent data,case-based reasoning,reasonable number,rough set,accuracy,noise,rough set theory,classification algorithms,case base reasoning,databases,cognition,machine learning,case based reasoning | Data mining,Pattern recognition,Computer science,Selection algorithm,Algorithm,Rough set,Artificial intelligence,Case-based reasoning,Statistical classification,Cognition,Machine learning | Conference |
Citations | PageRank | References |
1 | 0.67 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sai Ji | 1 | 164 | 13.37 |
| Shenfang Yuan | 2 | 76 | 12.49 |
| Shuiping Wang | 3 | 1 | 1.34 |