Hybrid feature ranking and classifier aggregation based on multi-criteria decision-making

作者：Wang, Xuetao; He, Qiang; Jian, Wanwei; Meng, Haoyu; Zhang, Bailin; Jin, Huaizhi; Yang, Geng; Zhu, Lin; Wang, Linjing^*; Zhen, Xin^*

来源：EXPERT SYSTEMS WITH APPLICATIONS, 2024, 238: 122193.

DOI：10.1016/j.eswa.2023.122193

• Ensemble classification
• Ensemble feature selection
• Feature ranking
• Ensemble diversity

This study introduces an ensemble methodology, namely, hybrid feature ranking and classifier aggregation (HyFraCa), to integrate ensemble feature selection and ensemble classification in a composite framework. The proposed HyFraCa is embedded in a multi-criteria decision-making (MCDM)-based scheme for feature ranking and classifier weighting, with an effective aggregation rule that yields a consensus feature ranking from ensembles of heterogeneous classifiers and feature selectors. Experimental evaluations on 20 public UCI datasets demonstrated the superiority of HyFraCa in producing a more accurate and generalizable classification compared with state-of-the-art benchmark ensemble methods. HyFraCa also provides robust and reliable consensus feature rankings, which are favorable for real-world classification problems in which feature interpretability is emphasized.

• 单位
  广州医学院; 南方医科大学; 广州中医药大学