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A novel random fast multi-label deep forest classification algorithm
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DOI：10.1016/j.neucom.2024.128903.png)
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With the development of big data, a large amount of multi-label data has emerged in various fields. The use of deep classification models to solve multi-label classification problems is the current research hotspot. However, the existing deep classification algorithms still face two great challenges in solving the multi-label classification problem: the poor multi-label classification accuracy and the high time cost. In order to solve the above challenges, this paper proposes a novel random fast multi-label deep forest classification algorithm (RFDF). Firstly, the label-by-label approach is adopted for the training and prediction of base classifiers, while the multi-label cascade model is constructed, so as to obtain better multi-label classification results. Then, an adaptive method is proposed to determine the number of base classifiers, and a random features partitioning strategy is provided to generate the initial base classifier, achieving parallel training of the base classifier. Finally, the proposed RFDF is compared with the existing advanced algorithms, and the experimental results show that the RFDF achieves better results on most of the metrics of the ten datasets, and obtains lower time cost compared to VDSDF and better stability.
Keyword:
Multi-label
Ensemble learning
Deep learning
Deep forest
Classification
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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