Name
Abstract | ||
|---|---|---|
Incremental feature selection can improve learning of accumulated data. We focus on incremental feature selection based on rough sets, which along with their generalizations (e.g., fuzzy rough sets), reduce dimensionality without requiring domain knowledge, such as data distributions. By analyzing the basic concepts of fuzzy rough sets on incremental datasets, we propose incremental mechanisms of information measure. Moreover, we introduce a key instance set containing representative instances to select supplementary features when new instances arrive. As the key instance set is much smaller than the whole dataset, the proposed incremental feature selection mostly suppresses redundant computations. We experimentally compare the proposed method with various non-incremental and two state-of-the-art incremental methods on a variety of datasets. The comparison results demonstrate that the proposed method achieves compact results with reduced computation time, especially on high-dimensional datasets. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.ins.2020.04.038 | Information Sciences |
Keywords | DocType | Volume |
Feature selection,Fuzzy rough set,Incremental learning,Information measure | Journal | 536 |
ISSN | Citations | PageRank |
0020-0255 | 11 | 0.42 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Peng Ni | 1 | 17 | 1.85 |
| Suyun Zhao | 2 | 22 | 3.17 |
| Xizhao Wang | 3 | 3593 | 166.16 |
| Hong Chen | 4 | 359 | 38.55 |
| Cuiping Li | 5 | 39 | 9.19 |
| E. C. C. Tsang | 6 | 714 | 31.47 |