RSS-LSTM: A Metaheuristic-Driven Optimization Approach for Efficient Text Classification

The digital data consumed by the average user daily is huge now and is increasing daily all over the world, which requires sophisticated methods to automatically process data, such as retrieving, searching, and formatting the data, particularly for classifying text data. Long Short-Term Memory (LSTM) is a prominent deep learning model for text classification. Several metaheuristic approaches, such as the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Firefly Algorithm (FF), have also been used to optimize Deep Learning (DL) models for classification. This study introduced an improved technique for text classification, called RSS-LSTM. The proposed technique optimized the hyperparameters and kernel function of LSTM through the Ringed Seal Search (RSS) algorithm to enhance simplification and learning ability. This work was also compared and evaluated against state-of-the-art techniques such as GA-LSTM, PSO-LSTM, and FF-LSTM. The results showed significantly better results using the proposed techniques, with an accuracy of 96%, recall of 96%, precision of 96%, and 95% f-measure on the Reuters-21578 dataset. In addition, it showed an accuracy of 77%, recall of 77%, precision of 78%, and f-measure of 76% on the 20 Newsgroups dataset, while it achieved accuracy, recall, precision, and f-measure of 91%, 91%, 94%, and 90%, respectively, using the AG News dataset.

@article{Nasir2024,
  title     = {RSS-LSTM: A Metaheuristic-Driven Optimization Approach for Efficient Text Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Muhammad Nasir and Noor Azah Samsudin and Shamsul Kamal Ahmad Khalid and Souad Baowidan and Humaira Arshad and Wareesa Sharif},
  doi       = {10.14569/IJACSA.2024.0150929},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150929}
}