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Improving imbalanced scientific text classification using sampling strategies and dictionaries
2011
Journal of Integrative Bioinformatics
Many real applications have the imbalanced class distribution problem, where one of the classes is represented by a very small number of cases compared to the other classes. One of the systems affected are those related to the recovery and classification of scientific documentation. Sampling strategies such as Oversampling and Subsampling are popular in tackling the problem of class imbalance. In this work, we study their effects on three types of classifiers (Knn, SVM and Naive-Bayes) when
doi:10.2390/biecoll-jib-2011-176
pmid:21926439
fatcat:r7oujlh3sbaxzdkcnxhauobsjm