TransFINN “Transparent Feature Integrated Neural Network for Text Feature Selection and Classification”

This paper introduces TransFINN, a transparent extension artificial neural network that combines transparent feature selection with text classification. TransFINN provides clear insights into which terms most influence its decisions, addressing the “black–box” limitation of conventional artificial neural networks. We evaluate TransFINN on AG News classification dataset and Yahoo! Answers Topic classification dataset, Two topic classification benchmark datasets. Results demonstrate that TransFINN achieves competitive classification accuracy and macro F1-score against baseline models while significantly improving feature-selection transparency. These findings indicate that TransFINN successfully narrows the gap between the prediction capability of artificial neural networks and the explainability needed for applications where the latter becomes paramount. This work opens up new directions for further research to explore how complex topic classification task could be improved, especially on religious data where a greater semantic insight is required.