This paper proposes a neural network prediction method based on multi-level POI category organization. Through the hierarchical aggregation of small sample ...
Mar 26, 2022 · Through the hierarchical aggregation of small sample categories, we established a POI category tree structure, which achieved a relatively ...
A hierarchical learning model for inferring the labels of points of interest with unbalanced data distribution. https://doi.org/10.1016/j.jag.2022.102751.
This encoder aims to generate hierarchical label embedding by fitting the label distribution and reducing the impact of data imbalance.
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Nov 21, 2019 · The reason that hierarchical models perform well, then, is (intuitively speaking) because there's no "true" imbalance.
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A hierarchical learning model for inferring the labels of points of interest with unbalanced data distribution · hmtl icon · Wenhao Yu, Jiaxin Chen, Cheng Wei.
Feb 10, 2022 · This paper presents a general prediction model to hierarchical multi-label classification, where the attributes to be inferred can be specified ...
Missing: unbalanced | Show results with:unbalanced
May 9, 2024 · The oversampling method offers several advantages in addressing imbalanced data, including increased representation of the minority class, ...
Missing: inferring | Show results with:inferring
May 2, 2023 · Many natural language processing (NLP) tasks are naturally imbalanced, as some target cate- gories occur much more frequently than others.
Missing: inferring | Show results with:inferring
Apr 9, 2023 · Given a set of labeled graphs Gℓ ⊆ G that are class-imbalanced, the goal is to learn a classifier f that assigns a class label to each unlabeled.