… Using SMOTE in Random Forest , SVM, and KNN algorithm The focus of this study is to improve
the accuracy of the text classification … of SMOTE process before classification using KNN, …

… , because these results are based on a precision close to 0,051. … improve the results, as in
the case of SVM classifier with Polynomial kernel and NLPBA dictionary, generally their use is …

… Similarly to the case of LDO we set n = length(di), so that … words contained in the document
being oversampled, which allows the … topic models to improve text categorization under class …

… (and in an especially accurate way for large values of n), which in our case coincides with
the corruption ratio . Since for all of our datasets and corruption ratios approximating Equation (…

… on SeqGAN can improve the performance of text classifier at … improves classification
accuracy by reducing the samples … in both indicators value, compared to the unsampled case. G-…

… of application but in our case a standard strategy is used. … we generated text and then
performed text classification using the … giving improved results for classes having low precision …

… than the simple sample-copy oversampling balancing. … improving the macro accuracy at
the expense of the micro one. In the end the decision about which metric to use in such cases …

… to improve the performance of imbalanced text classification. … case of imbalanced text
classification, we focus on precision … can correctly predict positive class sample. These values can …

… oversampling techniques leads to better performance, except in a few cases where under-sampling
techniques … the majority class's classification accuracy declined a little but improved …

… classifier diversity is leveraged to improve accuracy. The authors of [1] explore oversampling
techniques for … that some methods had low performance, like in the case of NMU, where the …