# A Study of Discriminatory Speech Classification Based on Improved Smote and SVM-RF
## 2 Related Work
### Imbalanced Data  
Imbalanced datasets are one of the most challenging problems in data mining and machine learning. Imbalanced data can cause a classifier to be biased toward the larger class, as the classifier will make predictions toward the majority class in order to minimize the overall classification error. The main approaches to deal with the data imbalance problem are algorithm-level and data-level approaches [19]. The algorithm-level approach is to modify the steps of the classification algorithm to achieve the goal of balanced data,such as [PERSON] et al. modifying the DT (Decision Tree) classifier [20] and [PERSON] et al. modifying the SVM classifier [21]. Data-level methods rebalance the minority and majority classes by modifying the number and content of the dataset, usually through undersampling or oversampling, such as the SMOTE method. SMOTE is a commonly used oversampling method for generating new data [22], which generates new data based on the similarity between minority class data. [PERSON] et al. used filtering feature selection based on fast correlation to remove irrelevant features in the field of disease classification, and used the SMOTE method to balance the dataset to improve accuracy. The results showed that the RF classifier performed well, achieving 99.35% AUC value and 97.81% accuracy [23]. [PERSON] et al. evaluated the performance of DT and SVM combined with SMOTE, respectively, in their study on the effectiveness of emotion analysis models. Indicators such as accuracy, precision, and F1 score all showed that these models could effectively distinguish positive and negative emotions, and SVM obtained 98.91% accuracy [24]. Their studies demonstrated the effectiveness of SMOTE in combination with SVM, RF, and in dealing with imbalanced dataset problems. There are also works to address data imbalances in discriminatory speech and related areas. For instance, [PERSON] and [PERSON] used four resampling methods, including Random Oversampling (ROS), SMOTE, Adaptive Synthetic (ADASYN), and Random Undersampling (RUS) to handle inequality of class distribution in a hate speech dataset [25]. [PERSON] and [PERSON] used the SVM model, SVM with various resampling methods, and the fine-tuned models to detect Indonesian hate speech posts in several imbalanced conditions, and the combination of SVM and SMOTE models performed the best in handling imbalanced problem based on the results [26]. [PERSON] et al. proposed a novel dual contrastive learning (DCL) framework for hate speech detection, which alleviated the problem of data imbalance by integrating the focal loss into the dual contrastive learning framework [27].  
In previous studies, both SVM and RF have been shown to have advantages in text classification and sentiment analysis tasks, as well as the effectiveness of SMOTE in dealing with imbalanced dataset problems. However, the current research still faces the challenge of how to deal with data imbalance and improve classification accuracy more effectively. Further, the discussion of discrimination in Chinese datasets is missing, which is also the problem that this paper is committed to solve.