Name
Title | ||
|---|---|---|
Multi-Label Bioinformatics Data Classification With Ensemble Embedded Feature Selection. |
Abstract | ||
|---|---|---|
In bioinformatics, the vast of multi-label type of datasets, including clinical text, gene, and protein data, need to be categorized. Specifically, due to the redundant or irrelevant features in bioinformatics data, the performance of multi-label classifiers will be limited, and therefore, selecting effective features from the feature space is necessary. However, most of the proposed methods, which aimed at dealing with multi-label feature selection problem in the past few years, only adopt a simple and direct strategy that transforms the multi-label feature selection problem into more single-label ones and ignore correlations among different labels. In this paper, a novel algorithm named ensemble embedded feature selection (EEFS) is proposed to handle multi-label bioinformatics data learning problem in a more effective and efficient way. The EEFS does not only explicitly find out the correlations among labels, but it can also adequately utilize the label correlations by multi-label classifiers and evaluation measures. Furthermore, it can reduce the accumulated errors of data itself by employing an ensemble method. The experimental results on five multi-label bioinformatics datasets show that our algorithm achieves significant superiority over the other state-of-the-art algorithms. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ACCESS.2019.2931035 | IEEE ACCESS |
Keywords | DocType | Volume |
Bioinformatics,multi-label learning,embedded feature selection | Journal | 7 |
ISSN | Citations | PageRank |
2169-3536 | 1 | 0.36 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yumeng Guo | 1 | 3 | 1.05 |
| Fu-lai Chung | 2 | 244 | 34.50 |
| Guo-Zheng Li | 3 | 368 | 42.62 |
| Lei Zhang | 4 | 1 | 0.36 |