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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Shibiao Wan Man-Wai Mak Bai Zhang Yue Wang Sun-Yuan Kung |
| Copyright Year | 2013 |
| Description | Author affiliation: Dept. of Pathology, Johns Hopkins Med. Instn., Baltimore, MD, USA (Bai Zhang) || Bradley Dept. of Electr. & Comput. Eng., Virginia Polytech. Inst. & State Univ., Arlington, VA, USA (Yue Wang) || Dept. of Electron. & Inf. Eng., Hong Kong Polytech. Univ., Hung Hom, China (Shibiao Wan; Man-Wai Mak) || Dept. of Electr. Eng., Princeton Univ., Princeton, NY, USA (Sun-Yuan Kung) |
| Abstract | In protein subcellular localization prediction, a predominant scenario is that the number of available features is much larger than the number of data samples. Among the large number of features, many of them may contain redundant or irrelevant information, causing the prediction systems suffer from overfitting. To address this problem, this paper proposes a dimensionality-reduction method that applies random projection (RP) to construct an ensemble multi-label classifier for predicting protein subcellular localization. Specifically, the frequencies of occurrences of gene-ontology terms are used as feature vectors, which are projected onto lower-dimensional spaces by random projection matrices whose elements conform to a distribution with zero mean and unit variance. The transformed low-dimensional vectors are classified by an ensemble of one-vs-rest multi-label support vector machine (SVM) classifiers, each corresponding to one of the RP matrices. The scores obtained from the ensemble are then fused for making the final decision. Experimental results on two recent datasets suggest that the proposed method can reduce the dimensions by six folds and remarkably improve the classification performance. |
| Sponsorship | IEEE Comput.Soc. |
| Starting Page | 35 |
| Ending Page | 42 |
| File Size | 283982 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781479913091 |
| DOI | 10.1109/BIBM.2013.6732715 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-12-18 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Proteins Support vector machines Protein subcellular localization Dimension reduction Accuracy Databases Multi-label classification Support vector machine classification Vectors Random projection Electronic mail |
| Content Type | Text |
| Resource Type | Article |
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