Enhanced Document Classification Using Ensemble Techniques

Both natural language processing and information retrieval rely on document classification. With the exponential growth of digital documents, there is an increasing demand for accurate and efficient document classification techniques. However, document classification faces several challenges, including processing vast amounts of data and extracting features from large volumes of unstructured data, which is complex. Moreover, numerous input features can be extracted, but not all of them are relevant to the classification problem. Single-classifier models often have limitations in capturing the complex patterns and nuances present in diverse document collections. In this paper, we propose a new approach to enhance document classification through the utilization of ensemble techniques. We combine multiple classifiers, including SVM, Naïve Bayes, and AdaBoost as base models, and aggregate their predictions with a neural network (NN) as a meta-model to improve overall classification performance. The suggested method aims to enhance accuracy, robustness, and generalization in classification by harnessing the diversity and complementary strengths of different classifiers. This research contributes to the field of document classification by utilizing the AdaBoost algorithm as a base model in stacking techniques applicable to document classification. The experimental results demonstrate that the proposed method outperforms the most recent methods in classifying documents in terms of accuracy, precision, and recall. The accuracy of the proposed system reached 87%, and the F1 score reached 87.8%.