Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26589
Title: Ensemble random projection for multi-label classification with application to protein subcellular localization
Authors: Wan, S
Mak, MW 
Zhang, B
Wang, Y
Kung, SY
Keywords: Dimension reduction
Multi-label classification
Protein subcellular localization
Random projection
Support vector machines
Issue Date: 2014
Publisher: IEEE
Source: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 4-9 May 2014, Florence, p. 5999-6003 How to cite?
Abstract: The curse of dimensionality severely restricts the predictive power of multi-label classification systems. High-dimensional feature vectors may contain redundant or irrelevant information, causing the classification systems suffer from overfitting. To address this problem, this paper proposes a dimensionality-reduction method that applies random projection (RP) to construct an ensemble of multilabel classifiers. The merits of the proposed method are demonstrated through a multi-label protein classification task. Specifically, high-dimensional feature vectors are extracted from protein sequences using the gene ontology (GO) and Swiss-Prot databases. The feature vectors are then 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 predicting the subcellular localization of proteins. Experimental results suggest that the proposed method can reduce the dimensions by seven folds and impressively improve the classification performance.
URI: http://hdl.handle.net/10397/26589
ISBN: 
DOI: 10.1109/ICASSP.2014.6854755
Appears in Collections:Conference Paper

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