Hypergraph-based importance assessment for binary classification data
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2023
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oai:localhost:PNK-73532023-03-31T01:23:41Z Hypergraph-based importance assessment for binary classification data Pawel, Misiorek Szymon, Janowski Hypergraph-based Importance hypergraph-based framework CC BY 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. 2023-03-31T01:23:41Z 2023-03-31T01:23:41Z 2023 Book https://link.springer.com/article/10.1007/s10115-022-01786-2 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7353 en application/pdf Springer |
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English |
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Hypergraph-based Importance hypergraph-based framework |
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Hypergraph-based Importance hypergraph-based framework Pawel, Misiorek Szymon, Janowski Hypergraph-based importance assessment for binary classification data |
description |
CC BY |
format |
Book |
author |
Pawel, Misiorek Szymon, Janowski |
author_facet |
Pawel, Misiorek Szymon, Janowski |
author_sort |
Pawel, Misiorek |
title |
Hypergraph-based importance assessment for binary classification data |
title_short |
Hypergraph-based importance assessment for binary classification data |
title_full |
Hypergraph-based importance assessment for binary classification data |
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Hypergraph-based importance assessment for binary classification data |
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Hypergraph-based importance assessment for binary classification data |
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hypergraph-based importance assessment for binary classification data |
publisher |
Springer |
publishDate |
2023 |
url |
https://link.springer.com/article/10.1007/s10115-022-01786-2 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7353 |
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1761912523496357888 |
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8.891145 |