Name
Title | ||
|---|---|---|
The practical method of fractal dimensionality reduction based on z-ordering technique |
Abstract | ||
|---|---|---|
Feature selection, the process of selecting a feature subset from the original feature set, plays an important role in a wide variety of contexts such as data mining, machine learning, and pattern recognition. Recently, fractal dimension has been exploited to reduce the dimensionality of the data space. FDR(Fractal Dimensionality Reduction) is one of the most famous fractal dimension based feature selection algorithm proposed by Traina in 2000. However, it is inefficient in the high dimensional data space for multiple scanning the dataset. Take advantage of the Z-ordering technique, this paper proposed an optimized FDR, ZBFDR(Z-ordering Based FDR), which can select the feature subset through scanning the dataset once except for preprocessing. The experimental results show that ZBFDR algorithm achieves better performance. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11811305_60 | ADMA |
Keywords | Field | DocType |
zbfdr algorithm,feature selection,data mining,original feature set,fractal dimensionality reduction,practical method,feature selection algorithm,feature subset,data space,optimized fdr,z-ordering technique,high dimensional data space,high dimensional data,pattern recognition,fractal dimension,machine learning | Data mining,Dimensionality reduction,Feature selection,Computer science,Artificial intelligence,Clustering high-dimensional data,Pattern recognition,Fractal dimension,Fractal,Curse of dimensionality,Feature extraction,Preprocessor,Machine learning | Conference |
Volume | ISSN | ISBN |
4093 | 0302-9743 | 3-540-37025-0 |
Citations | PageRank | References |
6 | 0.48 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guanghui Yan | 1 | 14 | 4.97 |
| Zhanhuai Li | 2 | 270 | 51.04 |
| Liu Yuan | 3 | 19 | 3.23 |