Thông tin tài liệu
Nhan đề : |
Hypergraph-based importance assessment for binary classification data |
Tác giả : |
Pawel, Misiorek Szymon, Janowski |
Năm xuất bản : |
2023 |
Nhà xuất bản : |
Springer |
Tóm tắt : |
We present a novel hypergraph-based framework enabling an assessment of the importance of binary classification data elements. Specifically, we apply the hypergraph model to rate data samples’ and categorical feature values’ relevance to classification labels. The proposed Hypergraph-based Importance ratings are theoretically grounded on the hypergraph cut conductance minimization concept. As a result of using hypergraph representation, which is a lossless representation from the perspective of higher-order relationships in data, our approach allows for more precise exploitation of the information on feature and sample coincidences. The solution was tested using two scenarios: undersampling for imbalanced classification data and feature selection. |
Mô tả: |
CC BY |
URI: |
https://link.springer.com/article/10.1007/s10115-022-01786-2 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7353 |
Bộ sưu tập |
OER - Công nghệ thông tin |
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