A Multi-label Filter Feature Selection Method Based on Approximate Pareto Dominance

The Pareto dominance has been applied to resolve the issue of choosing significant features from a multi-label dataset. High-dimensional labels will directly result in the difficulty of forming Pareto dominance. This work proposes a multi-label feature selection approach based on the approximate Pareto dominance (MAPD) to address this issue. It maps the multi-label feature selection to the problem of solving the approximate Pareto dominant solution set. By introducing an approximate parameter, it is possible to efficiently cut down on the amount of features in the chosen feature subset while also raising its quality. To verify the performance of MAPD, this research compares the MAPD algorithm with alternative approaches in terms of Hamming loss, accuracy, and chosen feature size using nine publicly available multi-label datasets. The findings indicate that the MAPD method performs better in terms of classification accuracy, Hamming loss, and the amount of features that may be chosen.

@article{Zhou2023,
  title     = {A Multi-label Filter Feature Selection Method Based on Approximate Pareto Dominance},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {7},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Jian Zhou and Yinnong Guo},
  doi       = {10.14569/IJACSA.2023.0140714},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140714}
}