Name
Abstract | ||
|---|---|---|
Aimed at the faults of K-nearest neighbor (KNN) algorithm in complex equipment's built-in test (BIT), an improved KNN (IKNN) algorithm is proposed to solve the problem from two aspects. Firstly, the weight of each input feature is learned using neural network to make important features contribute more in the classifications; this improves the precision of classification. Secondly, clustering each sample of the training set to reduce the data volume of training set, this improves the running speed of the algorithm. Simulation experiments prove the effectiveness of the IKNN algorithm with higher precision and less calculation. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/ICNSC.2008.4525257 | ICNSC |
Keywords | Field | DocType |
neural network,k nearest neighbor,simulation experiment | k-nearest neighbors algorithm,Training set,Computer science,Artificial intelligence,Artificial neural network,Cluster analysis,Machine learning,Built-in self-test | Conference |
Volume | Issue | ISSN |
null | 04 | null |
ISBN | Citations | PageRank |
978-1-4244-1686-8 | 1 | 0.38 |
References | Authors | |
6 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongchao Ji | 1 | 1 | 0.38 |
| Bifeng Song | 2 | 13 | 9.70 |
| Fei Han | 3 | 1 | 0.38 |