Name
Abstract | ||
|---|---|---|
High-dimension of feature space in text classification is a major problem of it. Feature selection is an effective method for feature reduction. A multiple centrifuge models based feature selection method is put forward in the view of the hypothesis that the same documents have core feature set in the text classification and the classes of the same high-frequency feature words of document have affinity. The proposed feature selection algorithm made a lot of innovation ideas in the field of feature reduction which improve the values of the low-frequency features in classification meanwhile ensuring the classification effect. The experiments in the Reuters-21578 corpus show that this method has better classification effect, and effectively improves the utilization of medium or low frequency features which have strong classification ability. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s10586-017-0812-9 | Cluster Computing |
Keywords | Field | DocType |
Centrifuge model, Qrderly whole class feature vector, Centroid feature set, Centrifuge matrix, Torque adjoint matrix | Data mining,Feature vector,Dimensionality reduction,Feature selection,Pattern recognition,Feature (computer vision),Computer science,Feature extraction,Feature (machine learning),Artificial intelligence,Kanade–Lucas–Tomasi feature tracker,Linear classifier | Journal |
Volume | Issue | ISSN |
20 | 2 | 1573-7543 |
Citations | PageRank | References |
0 | 0.34 | 24 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qinghu Wang | 1 | 0 | 0.34 |
| Lisha Liu | 2 | 0 | 0.34 |
| Jing-Qing Jiang | 3 | 57 | 9.05 |
| Mingyang Jiang | 4 | 0 | 0.34 |
| Yinan Lu | 5 | 19 | 6.62 |
| Zhili Pei | 6 | 58 | 6.64 |