Improving Chi-Square Feature Selection using a Bernoulli Model for Multi-label Classification of Indonesian-Translated Hadith

Hadith is the foundational knowledge in Islam that must be studied and practiced by Muslims. In the Hadith, several types of teachings are beneficial to Muslims and all of mankind. Some Hadith serve as advice, while others contain prohibitions that Muslims should adhere to. There are yet others that do not belong to these categories and serve only as information. This study focuses on increasing the performance of Chi-Square feature selection to obtain relevant features for multilabel classification of Indonesian-translated Bukhari Hadith data. This study proposes a Chi-Square-based Bernoulli model to improve Chi-Square feature selection which is appropriate for short-text data such as Hadith. The findings of this study show that the proposed method can select relevant features based on data classes; thereby improving Hadith classification performance with an error value of 9.38% compared to that (9.91%) obtained using the basic Chi-Square feature selection.

@article{Nurfikri2021,
  title     = {Improving Chi-Square Feature Selection using a Bernoulli Model for Multi-label Classification of Indonesian-Translated Hadith},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {12},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Fahmi Salman Nurfikri and Adiwijaya},
  doi       = {10.14569/IJACSA.2021.0121268},
  url       = {https://doi.org/10.14569/IJACSA.2021.0121268}
}