Improving minority prediction performance of support vector machine for imbalanced text data via feature selection and SMOTE

Text data is usually made up of a wide variety of unique words.Even in standard text data, it is common to find tens of thousands of different words.In text data analysis, usually, each unique word is treated as a variable.Thus, text data can be regarded as a dataset with a large number of variables.On the other hand, in text data classification, we often encounter class label imbalance problems.In the cases of substantial imbalances, the performance of conventional classification models can be severely degraded.To improve the classification performance of support vector machines (SVM) for imbalanced data, algorithms such as the Synthetic Minority Over-sampling Technique (SMOTE) can be used.The SMOTE algorithm synthetically generates new observations for the minority class based on the k-Nearest Neighbors (kNN) algorithm.However, in datasets with a large number of variables, such as text data, errors may accumulate.This can potentially impact the performance of the kNN algorithm.In this study, we propose a method for enhancing prediction performance for the minority class of imbalanced text data.Our approach involves employing variable selection to generate new synthetic observations in a reduced space, thereby improving the overall classification performance of SVM.