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Multi-Label Bioinformatics Data Classification With Ensemble Embedded Feature Selection
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DOI：10.1109/ACCESS.2019.2931035.png)
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摘要
En 中文
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.
Keyword:
Bioinformatics
multi-label learning
embedded feature selection
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期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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