Name
Title | ||
|---|---|---|
Attribute Reduction Based on Bi-directional Distance Correlation and Radial Basis Network |
Abstract | ||
|---|---|---|
Attribute reduction is one of the important means to improve the efficiency and the quality of data mining, especially for high dimension data. From the view of distance and correction , the bi-directional distance and correction method was presented. This method can be used to measure the importance of dada attributes. Moreover, the revised decrease-increase combination strategy was used to reduce dimensionality and the radial basis neural network was used to validate the sub-set. This method adopts appropriate correlation function according to sample characteristic, which can avoid the limitation of IOC method. Since the longitudinal input-output connection and the horizontal difference between attribute and target was taken into account, the measure of the attribute importance will be more rational. So, quality data will be supply for the process of data mining subsequently. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72393-6_78 | ISNN (2) |
Keywords | Field | DocType |
attribute reduction,high dimension data,data mining,ioc method,bi-directional distance correlation,appropriate correlation function,bi-directional distance,quality data,attribute importance,dada attribute,radial basis network,correction method,input output,correlation function,neural network | Data mining,Radial basis network,Pattern recognition,Computer science,Distance correlation,Curse of dimensionality,Artificial intelligence,Correlation function,Artificial neural network,Machine learning | Conference |
Volume | ISSN | Citations |
4492 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 4 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Li-Chao Chen | 1 | 14 | 7.02 |
| Wei Zhang | 2 | 440 | 72.00 |
| Ying Jun (Angela) Zhang | 3 | 1905 | 135.63 |
| Bin Ye | 4 | 0 | 0.34 |
| Lihu Pan | 5 | 1 | 1.39 |
| Jing Li | 6 | 130 | 9.47 |