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Cost-Sensitive Hypergraph Learning With F-Measure Optimization
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AI
DOI：10.1109/TCYB.2021.3126756.png)
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Abstract
En 中文
The imbalanced issue among data is common in many machine-learning applications, where samples from one or more classes are rare. To address this issue, many imbalanced machine-learning methods have been proposed. Most of these methods rely on cost-sensitive learning. However, we note that it is infeasible to determine the precise cost values even with great domain knowledge for those cost-sensitive machine-learning methods. So in this method, due to the superiority of F-measure on evaluating the performance of imbalanced data classification, we employ F-measure to calculate the cost information and propose a cost-sensitive hypergraph learning method with F-measure optimization to solve the imbalanced issue. In this method, we employ the hypergraph structure to explore the high-order relationships among the imbalanced data. Based on the constructed hypergraph structure, we optimize the cost value with F-measure and further conduct cost-sensitive hypergraph learning with the optimized cost information. The comprehensive experiments validate the effectiveness of the proposed method.
Keywords:
Costs
Optimization
Learning systems
Cybernetics
Task analysis
Research and development
Hyperspectral imaging
Cost-sensitive
F-measure optimization
hypergraph learning
imbalanced data
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W