Improving Classification of Protein Interaction Articles Using Context Similarity-Based Feature Selection

BioMed Research International
Yifei ChenBing-Qing Han

Abstract

Protein interaction article classification is a text classification task in the biological domain to determine which articles describe protein-protein interactions. Since the feature space in text classification is high-dimensional, feature selection is widely used for reducing the dimensionality of features to speed up computation without sacrificing classification performance. Many existing feature selection methods are based on the statistical measure of document frequency and term frequency. One potential drawback of these methods is that they treat features separately. Hence, first we design a similarity measure between the context information to take word cooccurrences and phrase chunks around the features into account. Then we introduce the similarity of context information to the importance measure of the features to substitute the document and term frequency. Hence we propose new context similarity-based feature selection methods. Their performance is evaluated on two protein interaction article collections and compared against the frequency-based methods. The experimental results reveal that the context similarity-based methods perform better in terms of the F1 measure and the dimension reduction rate. Benefiting from...Continue Reading

References

Mar 15, 1995·Statistics in Medicine·M A Jaro
Aug 28, 2007·Bioinformatics·Yvan SaeysPedro Larrañaga

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BETA
feature extraction

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LIBSVM
SVM poly
GI
poly
SVM

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