Hybrid feature ranking and classifier aggregation based on multi-criteria decision-making
2023; Elsevier BV; Volume: 238; Linguagem: Inglês
10.1016/j.eswa.2023.122193
ISSN1873-6793
AutoresXuetao Wang, Qiang He, Wanwei Jian, Haoyu Meng, Bailin Zhang, Huaizhi Jin, Geng Yang, Zhu Lin, Linjing Wang, Xin Zhen,
Tópico(s)Fuzzy Logic and Control Systems
ResumoThis 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.
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