Name
Abstract | ||
|---|---|---|
Feature selection (FS) is a well-studied area that avoids issues related the curse of dimensionality and overfitting. FS is a preprocessing procedure that identifies the feature subset that is both relevant and non-redundant. Although FS has been driven by the exploration of “big data” and the development of high-performance computing, the implementation of scalable information-theoretic FS remains an under-explored topic. In this contribution, we revisit the greedy optimization procedure of information-theoretic filter FS and propose a semi-parallel optimizing paradigm that can provide an equivalent feature set as the greedy FS algorithms in a fraction of the time. We focus on greedy selection algorithms due to their larger computational complexity associated with a rapidly growing number of features. Our framework is benchmarked against twelve datasets, including one extremely large dataset that has more than a million features, and we show our framework can significantly speed up the process of FS while selecting nearly the same features as the state-of-the-art information-theoretic FS methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.ins.2019.03.075 | Information Sciences |
Keywords | Field | DocType |
Feature selection,Information theory,Parallel computing | Feature selection,Curse of dimensionality,Preprocessor,Artificial intelligence,Overfitting,Big data,Mathematics,Machine learning,Computational complexity theory,Speedup,Scalability | Journal |
Volume | ISSN | Citations |
492 | 0020-0255 | 1 |
PageRank | References | Authors |
0.38 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heng Liu | 1 | 153 | 27.10 |
| Gregory Ditzler | 2 | 214 | 16.55 |