Name
Abstract | ||
|---|---|---|
Feature selection is a nontrivial preprocessing technique in many practical application domains. There are three key challenges with respect to real-world data. Firstly, the dimensionality of data keeps growing and will achieve hundreds of millions. Secondly, the data has the characteristic of high-dimensional and small-size. Thirdly, practical applications need to process each feature in an online manner. However, most of the previous methods only pay much attention to solving the challenges of high dimensionality and online stream. To address all issues above, we propose OFSI, in this article, an O_nline streaming F_eature S_election based on feature I_nteraction method for feature selection. OFSI can effectively select the streaming features that are strongly related to each other in high-dimensional and small-size data, via using feature interaction. Furthermore, to address upcoming features that arrive by groups, we present a new group-OFSI algorithm for online group feature selection. An extensive experiment using a series of benchmark data sets shows that the proposed two algorithms, OFSI and group-OFSI, outperform six state-of-the-art online streaming feature selection methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1002/cpe.6435 | CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE |
Keywords | DocType | Volume |
feature interaction, group features, high-dimensional and small-size, online feature selection, streaming feature | Journal | 33 |
Issue | ISSN | Citations |
21 | 1532-0626 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Lv | 1 | 0 | 0.34 |
| Yaojin Lin | 2 | 470 | 23.01 |
| Xiangyan Chen | 3 | 0 | 0.34 |
| Chenxi Wang | 4 | 30 | 2.29 |
| Shaozi Li | 5 | 403 | 54.27 |