IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise classification and support vector machines
Advances in kernel methods
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Prediction of protein subcellular localizations using moment descriptors and support vector machine
PRIB'06 Proceedings of the 2006 international conference on Pattern Recognition in Bioinformatics
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Mathematical and Computer Modelling: An International Journal
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Theoretical and computational methods for the prediction of protein subcellular localization have been proposed and are developing continuously. Many representations of protein sequence are proposed but a new problem arises: how to organize them together to improve prediction. It is an available solution to serialize multiple representations to single bigger one, but is still hard to avoid calculation error derived from greatly different feature values and causes huge computational burden natively because of high dimensional feature vector. We present a novel method based on decision templates(DT) for such problems in this paper. First, a protein sequence is represented as three new types of feature vectors. Then, the feature vectors are further taken as the inputs of individual SVM classifiers respectively. Finally, the outputs of these classifiers are aggregated by decision templates. The results demonstrate that DT is superior to other methods of subcellular localization prediction.