Optimizing Document Classification Using Modified Relative Discrimination Criterion and RSS-ELM Techniques

Internet content is increasing daily, and more data are being digitized due to technological advancements. Ever-increasing textual data in words, phrases, terms, sentences, and paragraphs pose significant challenges in classifying them effectively and require sophisticated techniques to arrange them automatically. The vast amount of textual data presents an opportunity to organise and extract valuable insights by identifying crucial pieces of information using feature selection techniques. Our article proposes “a Modified Relative Discrimination Criterion (MRDC) Technique and Ringed Seal Search-Extreme Learning Machine (RSS-ELM) to improve document classification", which prioritizes key data and fits corresponding documents into appropriate classes. The proposed MRDC and RSS-ELM techniques are compared with several existing techniques, such as the Relative Discrimination Criterion (RDC), the Improved Relative Discrimination Criterion (IRDC), GA-EM, and CS-ELM. The MRDC technique produced superior classification results with 91.60% accuracy compared to existing RDC and IRDC for feature selection. Moreover, the RSS-ELM optimization technique improved predictions significantly, with 98.9% accuracy compared to CS-ELM and GA-ELM on the Reuter21578 dataset.

@article{Anwaar2025,
  title     = {Optimizing Document Classification Using Modified Relative Discrimination Criterion and RSS-ELM Techniques},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {4},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Muhammad Anwaar and Ghulam Gilanie and Abdallah Namoun and Wareesa Sharif},
  doi       = {10.14569/IJACSA.2025.0160482},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160482}
}