Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Gene ontology annotation as text categorization: An empirical study
Information Processing and Management: an International Journal
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Gene Functional Annotation with Dynamic Hierarchical Classification Guided by Orthologs
DS '09 Proceedings of the 12th International Conference on Discovery Science
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
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In recent years, a number of machine learning approaches to literature-based gene function annotation have been proposed. However, due to issues such as lack of labeled data, class imbalance and computational cost, they have usually been unable to surpass simpler approaches based on string-matching. In this paper, we investigate the use of semantic kernels as a way to address the task's inherent data scarcity and we propose a simple yet effective solution to deal with class imbalance. From experiments on the TREC Genomics Track data, our approach achieves better F1-score than two state-of-the-art approaches based on string-matching and cross-species information.