Name
Abstract | ||
|---|---|---|
The task of feature selection is to select a subset of the original features according to certain predefined criterion with the goal to remove irrelevant and redundant features, improve the prediction performance and reduce the computational costs of data mining algorithms. In this paper, we integrate feature relevance and redundancy explicitly in the feature selection criterion. Spectral feature analysis is applied here which can fit into both supervised and unsupervised learning problems. Specifically, we formulate the problem into a combinatorial problem to maximize the relevance and minimize the redundancy of the selected subset of features at the same time. The problem can be relaxed and solved with an efficient extended power method with global convergence guaranteed. Extensive experiments demonstrate the advantages of the proposed technique in terms of improving the prediction performance and reducing redundancy in data. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICDM.2015.121 | IEEE International Conference on DataMining |
Keywords | Field | DocType |
spectral feature selection, relevance, redundancy, eigen-optimization, supervised/unsupervised learning | Convergence (routing),Data mining,Dimensionality reduction,Feature selection,Feature (computer vision),Computer science,Redundancy (engineering),Unsupervised learning,Minimum redundancy feature selection,Artificial intelligence,Machine learning,Pattern recognition (psychology) | Conference |
ISSN | Citations | PageRank |
1550-4786 | 2 | 0.37 |
References | Authors | |
13 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Linli Xu | 1 | 790 | 42.51 |
| Qi Zhou | 2 | 2 | 0.37 |
| Aiqing Huang | 3 | 7 | 0.81 |
| Wenjun Ouyang | 4 | 2 | 0.71 |
| Enhong Chen | 5 | 1235 | 86.93 |