INTEGRATION OF LAYER-WISE RELEVANCE PROPAGATION, RECURSIVE DATA PRUNING, AND CONVOLUTIONAL NEURAL NETWORKS FOR IMPROVED TEXT CLASSIFICATION

Abstract

This research presents a significant advancement in text classification by integrating Layer-wise Relevance Propagation (LRP), recursive data pruning, and Convolutional Neural Networks (CNNs) with cross-validation. The study addresses the critical limitations of existing text classification methods, particularly issues of information loss and overfitting, which often hinder the efficiency and interpretability of models in natural language processing (NLP). To overcome these challenges, the proposed model employs LRP to enhance the interpretability of the classification process, allowing for precise identification of relevant features that contribute to decision-making. Additionally, the implementation of recursive data pruning optimizes model efficiency by dynamically eliminating irrelevant or redundant data, thereby reducing computational complexity without compromising performance. The effectiveness of the approach is further bolstered by utilizing cross-validation techniques to ensure robust evaluation across diverse datasets. The empirical evaluation of the integrated model revealed remarkable improvements in classification performance, achieving an accuracy of 94%, surpassing the benchmark of 92.88% established by the ReDP-CNN model proposed by Li et al. (2020). The comprehensive assessment included detailed metrics such as precision, recall, and F1-score, confirming the model's robust capability in accurately classifying text data across various categories.