TSC-SVM framework.

<div><p>In credit risk assessment, unsupervised classification techniques can be introduced to reduce human resource expenses and expedite decision-making. Despite the efficacy of unsupervised learning methods in handling unlabeled datasets, their performance remains limited owing to cha...

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Bibliographic Details
Main Author:	Tao Yu (176965) (author)
Other Authors:	Wei Huang (36889) (author), Xin Tang (134573) (author), Duosi Zheng (20542560) (author)
Published:	2025
Subjects:	Biotechnology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified unsupervised learning methods unsupervised imbalance problem unsupervised classification techniques three distinct perspectives stage hybrid system parameter adjustment complexities method mitigates discrepancies handling unlabeled datasets credit risk assessment thoroughly mine data imbalanced data xlink "> world applications various real svm model svm ), spectral clustering proposed algorithm prediction outcomes paper introduces local optima label predictions financial institutions expedite decision effectively addresses customer base
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TSC-SVM framework.

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