Name
Abstract | ||
|---|---|---|
K-Nearest Neighbor is used broadly in text classification, but it has one deficiency—computational efficiency. In this paper, we propose a heuristic search way to find out the k nearest neighbors quickly. Simulated annealing algorithm and inverted array are used to help find out the expected neighbors. Our experimental results demonstrate a significant improvement in classification computational efficiency in comparison with the conventional KNN. |
Year | Venue | Keywords |
|---|---|---|
2007 | DMIN | simulated annealing,heuristic search,k nearest neighbor,simulated annealing algorithm |
Field | DocType | Citations |
k-nearest neighbors algorithm,Simulated annealing,Heuristic,Pattern recognition,Computer science,Adaptive simulated annealing,Artificial intelligence | Conference | 1 |
PageRank | References | Authors |
0.36 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chuanyao Yang | 1 | 11 | 2.32 |
| Yuqin Li | 2 | 4 | 1.82 |
| Zhang Chenghong | 3 | 22 | 7.07 |
| Yunfa Hu | 4 | 74 | 13.44 |