Name
Abstract | ||
|---|---|---|
Recent years, various information theoretic based measurements have been proposed to remove redundant features from high-dimensional data set as many as possible. However, most traditional Information-theoretic based selectors will ignore some features which have strong discriminatory power as a group but are weak as individuals. To cope with this problem, this paper introduces a cooperative game theory based framework to evaluate the power of each feature. The power can be served as a metric of the importance of each feature according to the intricate and intrinsic interrelation among features. Then a general filter feature selection scheme is presented based on the introduced framework to handle the feature selection problem. To verify the effectiveness of our method, experimental comparisons with several other existing feature selection methods on fifteen UCI data sets are carried out using four typical classifiers. The results show that the proposed algorithm achieves better results than other methods in most cases. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.patcog.2012.02.001 | Pattern Recognition |
Keywords | Field | DocType |
redundant feature,high-dimensional data,feature selection problem,better result,existing feature selection method,strong discriminatory power,fifteen uci data set,feature evaluation,general filter feature selection,proposed algorithm,cooperative game theory,machine learning,feature selection | Data mining,Data set,Pattern recognition,Feature selection,Feature (computer vision),Feature evaluation,Cooperative game theory,Artificial intelligence,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
45 | 8 | 0031-3203 |
Citations | PageRank | References |
31 | 1.01 | 45 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Sun | 1 | 277 | 17.12 |
| Yanheng Liu | 2 | 228 | 36.14 |
| Jin Li | 3 | 48 | 1.73 |
| Jianqi Zhu | 4 | 62 | 5.74 |
| Hui-Ling Chen | 5 | 655 | 26.09 |
| Xue-jie Liu | 6 | 50 | 4.49 |