Optimal Feature Selection for Imbalanced Text Classification

Textual data suffers from two main problems, large number of features and class imbalance. Many conventional approaches and their variants exist in literature to solve both these problems. The classic synthetic minority oversampling technique (SMOTE) is the most explored technique for balancing the dataset. We introduced a new algorithm to balance the dataset, named distributed SMOTE (D_SMOTE), which overcomes the problem of lack of density and reducing the formation of small disjuncts. Further, another problem handled is the large number of features or high-dimensionality. To solve high-dimensionality, a novel feature selection technique is introduced known as modified biogeography-based optimization (M_BBO). The proposed model, M_BBO, performs modification in ranking of variables using feature weighting algorithm rather than randomly ranking. We have proposed two new expressions in D_SMOTE and one new expression in M_BBO. The extensive experimental results are computed out on four text classification datasets with four machine learning classifiers. The results are concluded using three performance measures, area under curve, G-mean, and F1-score. Our empirical and statistical observation for four class-imbalanced datasets shows that the proposed D_SMOTE outperforms the other similar oversampling technique. We have also compared our proposed algorithm, M_BBO+D_SMOTE, with other models on 17 imbalanced text classification datasets. Our model outperformed the other models in 14 datasets. We have also compared our model with bidirectional encoder representations from transformers. To validate the experimental analysis, statistical Friedman test is employed.