Ze-HFS: zentropy-based uncertainty measure for heterogeneous feature selection and knowledge discovery
Küçük Resim Yok
Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
IEEE computer society
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Knowledge discovery of heterogeneous data is an active topic in knowledge engineering. Feature selection for heterogeneous data is an important part of effective data analysis. Although there have been many attempts to study the feature selection for heterogeneous data, there are still some challenges, such as the unbalanced problem between the stability and validity of the designed model. Hence, this paper focuses on how to design an effective and robust heterogeneous feature selection method, namely a zentropy-based uncertainty measure for heterogeneous feature selection(Ze-HFS). Different from other entropy-based uncertainty measures, the proposed method does not consider single-level information measures but systematically analyzes and integrates the information between different granular levels, which has an obvious advantage in the study of heterogeneous data knowledge discovery. Specifically, a heterogeneous distance metric is first introduced to construct heterogeneous neighborhood granules and heterogeneous neighborhood rough sets(HNRS). Then, the zentropy-based uncertainty measure is developed by analyzing the granular level structure in the HNRS model. Finally, two significant measures based on the above research are designed for heterogeneous feature selection. Compared with other state-of-the-art methods, the experimental results on 18 public datasets demonstrate the robustness and effectiveness of the proposed method.
Açıklama
Anahtar Kelimeler
Data Mining, Feature Selection, Granular Computing, Rough Set, Uncertainty Measure
Kaynak
IEEE transactions on knowledge and data engineering
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
36
Sayı
11
Künye
Yuan, K., Miao, D., Pedrycz, W., Ding, W., & Zhang, H. (2024). Ze-HFS: Zentropy-based uncertainty measure for heterogeneous feature selection and knowledge discovery. IEEE Transactions on Knowledge and Data Engineering.