ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2019, Vol. 56 ›› Issue (8): 1670-1676.doi: 10.7544/issn1000-1239.2019.20190332

Special Issue: 2019人工智能前沿进展专题

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Deep Forest for Multiple Instance Learning

Ren Jie, Hou Bojian, Jiang Yuan   

  1. (National Key Laboratory for Novel Software Technology (Nanjing University), Nanjing 210023) (Collaborative Innovation Center of Novel Software Technology and Industrialization (Nanjing University), Nanjing 210023)
  • Online:2019-08-01

Abstract: Multi-instance learning has been applied to various tasks, such as image retrieval, text classification, face recognition, etc. Deep neural network has also been successfully applied to plenty of tasks and problems. MI-Nets are one of the successful applications to multi-instance learning of deep neural network. Although MI-Nets have obtained achievements and the main task they are good at is image task, while on non-image tasks, they show mediocre performance. Over the last two years, deep forest has achieved good performance on non-image tasks and is favored for its less parameters and steady performance compared with deep neural network. Thus it is urgent and necessary to apply deep forest to multi-instance learning. However, due to the limitation of the structure of deep forest, we cannot simply substitute the bag-level forest for each forest of deep forest. Therefore, we need to change the structure of deep forest to achieve our purpose. In this paper, we provide a new structure of deep forest, that is multiple instance deep forest (MIDF). We regard each instance from a bag as a new bag, and thus the distribution output from the middle level can concatenate the original bag to make the cascade structure valid. We can also assure the number of layers of MIDF. Experimental results show that our method has comparable performance with MI-Nets on image task, while on non-image tasks, our method outperforms MI-Nets and other baseline methods.

Key words: machine learning, multi-instance learning, deep forest, supervised learning, non-image categorization

CLC Number: