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- W4285241821 abstract "Multiclass classification is dissimilar from binary classification based on the number of output classes. It can be implemented by using subsequent methods such as extended from the binary case, translate the multiclass into binary-class classification problems and hierarchical classification methods. In this paper, multiclass hierarchical fuzzy rule-based classification tree for using quantitative transactions for correlated pattern mining is proposed. It uses different fuzzy membership linguistic operator values for each fuzzy data item to generate fuzzy classification patterns from multi-labeled data set. The result of experiments shows that the MHFRB mining algorithm is used to generate fuzzy classification rules and efficiently classify infrequent patterns from multi-labeled data." @default.
- W4285241821 created "2022-07-14" @default.
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- W4285241821 date "2022-01-01" @default.
- W4285241821 modified "2023-09-26" @default.
- W4285241821 title "Multiclass Hierarchical Fuzzy Classification on Multi-labeled Data" @default.
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- W4285241821 doi "https://doi.org/10.1007/978-981-19-0898-9_24" @default.
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