Random forest dissimilarity based multi-view learning for Radiomics application

作者：

Highlights：

• Propose a Random forest dissimilarity based method for multi-view learning.

• Study the effect of hyperparameters on the quality of random forest dissimilarity.

• Compare the proposed method to the state of art Radiomics solutions.

• Compare the proposed method to multi-view learning approaches

• Show that the proposed approach outperforms the state-of-the-art methods.

摘要

•Propose a Random forest dissimilarity based method for multi-view learning.•Study the effect of hyperparameters on the quality of random forest dissimilarity.•Compare the proposed method to the state of art Radiomics solutions.•Compare the proposed method to multi-view learning approaches•Show that the proposed approach outperforms the state-of-the-art methods.

论文关键词：Radiomics,Dissimilarity space,Random forest,Machine learning,Feature selection,Multi-view learning,High dimension,Low sample size

论文评审过程：Received 21 February 2018, Revised 28 September 2018, Accepted 16 November 2018, Available online 20 November 2018, Version of Record 23 November 2018.

论文官网地址：https://doi.org/10.1016/j.patcog.2018.11.011