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| Multi-label classifier chain method based on gradient boosting |
| Key Laboratory of Data Engineering and Visual Computing, Chongqing University of Posts and Telecommunications, Chongqing 400065, China |
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Abstract To determine the chain order of the multi-label classifier chain algorithm and mine the high-level label corelation, a multi-label classifier chain method was proposed based on gradient boosting. The overall framework of GBCC was given, and the gradient boosting of a single label was conducted by pre-pruning strategy. In the process, the label confidence and error evaluation scores were used to determine the optimal chain order, and the label transfer and feature transfer were performed among labels to mine the high-level label relevance. The proposed method was compared with 4 classifier chain algorithms of CC, ECC, OCC and EOCC and 4 multi-label classification algorithms of BR, HOMER, MLKNN and CLR on 12 multi-label data sets such as bibtex and Corel5k. The results show that the new method can effectively improve the prediction performance under various evaluation indicators of micro-F1, macro-F1, Hamming loss and One-error with maintaining the flexibility of classifier chain algorithm.
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Received: 24 December 2019
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2021, Vol. 42 
