Extended Max-Occurrence with Normalized Non-Occurrence as MONO Term Weighting Modification to Improve Text Classification

The increased volume of data due to advancements in the internet and relevant technology makes text classification of text documents a popular demand. Providing better representations of the feature vector by setting appropriate term weight values using supervised term weighting schemes improves classification performance in classifying text documents. A state-of-the-art term weighting scheme MONO with variants TF-MONO and SRTF-MONO improves text classification considering the values of non-occurrences. However, the MONO strategy suffers setbacks in weighting terms with non-uniformity values in its term's interclass distinguishing power. In this study, extended max-occurrence with normalized non-occurrence (EMONO) with variants TF-EMONO and SRTF-EMONO are proposed where EMO value is determined as MO interclass extensions as improvements to address its problematic weighting behavior of MONO as it neglected the utilization of the occurrence of the classes with short-distance document frequency in non-uniformity values. The proposed schemes' classification performance is compared with the MONO variants on the Reuters-21578 dataset with the KNN classifier. Chi-square-max was used to conduct experiments in different feature sizes using micro-F1 and macro-F1. The results of the experiments explicitly showed that the proposed EMONO outperforms the variants of MONO strategy in all feature sizes with an EMO parameter value of 2 sets number of classes in MO extension. However, the SRTF-EMONO showed better performance with Micro-F1 scores of 94.85% and 95.19% for smallest to largest feature size, respectively. Moreover, this study also emphasized the significance of interclass document frequency values in improving text classification aside from non-occurrence values in term weighting schemes.

@article{Abalorio2022,
  title     = {Extended Max-Occurrence with Normalized Non-Occurrence as MONO Term Weighting Modification to Improve Text Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {4},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Cristopher C. Abalorio and Ariel M. Sison and Ruji P. Medina and Gleen A. Dalaorao},
  doi       = {10.14569/IJACSA.2022.0130411},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130411}
}