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- W2611702754 abstract "In this paper, the problem of multilabel classification using the classifier chain scheme is addressed. We deal with the problem of building a diverse ensemble of the classifier-chain-based ensemble. For this purpose, we propose a permutation-based criterion of chain diversity. The final ensemble is build using a multi-objective genetic algorithm, which is used to optimise classification quality and chain diversity simultaneously. The proposed methods were evaluated using 29 benchmark datasets. The comparison was performed using four different multi-label evaluation measures. The experimental study reveals that the proposed approach provides a better classification quality than response-based diversity criteria." @default.
- W2611702754 created "2017-05-12" @default.
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- W2611702754 date "2017-05-07" @default.
- W2611702754 modified "2023-09-27" @default.
- W2611702754 title "Permutation-Based Diversity Measure for Classifier-Chain Approach" @default.
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- W2611702754 doi "https://doi.org/10.1007/978-3-319-59162-9_43" @default.
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