Name
Abstract | ||
|---|---|---|
Instance-based learning methods such as the nearest neighbor classifier have proven to perform well in pattern classification in several fields. Despite their high classification accuracy, they suffer from a high storage requirement, computational cost, and sensitivity to noise. In this paper, we present a data reduction method for instance-based learning, based on entropy-based partitioning and representative instances. Experimental results show that the new algorithm achieves a high data reduction rate as well as classification accuracy. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11751595_63 | ICCSA (3) |
Keywords | Field | DocType |
instance-based learning,high storage requirement,entropy-based partitioning,data reduction method,high data reduction rate,pattern classification,classification accuracy,instance-based learning method,computational cost,high classification accuracy,data reduction,instance based learning | Data mining,Nearest neighbour,Instance-based learning,Pattern recognition,Computer science,Euclidean distance measure,Artificial intelligence,Data reduction,Nearest neighbor classifier | Conference |
Volume | ISSN | ISBN |
3982 | 0302-9743 | 3-540-34075-0 |
Citations | PageRank | References |
10 | 0.65 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Seung-Hyun Son | 1 | 19 | 2.39 |
| Jae-yearn Kim | 2 | 44 | 4.67 |