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论文类型：期刊论文

发表时间：2011-08-01

发表刊物：COMPUTERS IN BIOLOGY AND MEDICINE

收录刊物：Scopus、SCIE、EI

卷号：41

期号：8

页面范围：648-652

ISSN号：0010-4825

关键字：Subcellular compartment; Support vector machines; Outlier mining; Principal component analysis

摘要：Automated prediction of protein subcellular localization is an important tool for genome annotation and drug discovery, and Support Vector Machines (SVMs) can effectively solve this problem in a supervised manner. However, the datasets obtained from real experiments are likely to contain outliers or noises, which can lead to poor generalization ability and classification accuracy. To explore this problem, we adopt strategies to lower the effect of outliers. First we design a method based on Weighted SVMs, different weights are assigned to different data points, so the training algorithm will learn the decision boundary according to the relative importance of the data points. Second we analyse the influence of Principal Component Analysis (PCA) on WSVM classification, propose a hybrid classifier combining merits of both PCA and WSVM. After performing dimension reduction operations on the datasets, kernel-based possibilistic c-means algorithm can generate more suitable weights for the training, as PCA transforms the data into a new coordinate system with largest variances affected greatly by the outliers. Experiments on benchmark datasets show promising results, which confirms the effectiveness of the proposed method in terms of prediction accuracy. (C) 2011 Elsevier Ltd. All rights reserved.