关键词: 随机子空间集成, 差异性度量, 集成学习, 机器学习

Abstract: Random subspace ensemble method is an essential part of ensemble learning research. It constructs several base learners on randomly selected feature subspaces, and finds a suitable way to combine the results of these base learners to give a final result. Random subspace ensemble method is much suitable for datasets with much higher feature dimensions than samples. However, because of the high feature dimensions, the ensemble model should sample numerous subspaces but cannot keep enough diversity between these subspaces, which causes low efficiency and bad performance. This paper proposes a diverse random subspace ensemble method without supervision and training. This method uses the multi-kernel MMD (maximum mean discrepancy) as similarity measure of subspace, and uses the spectral clustering algorithm on high similarity subspaces to select a representative subspace among lots of random subspaces with similar distribution structure. The experimental results demonstrate the effectiveness and efficiency of the proposed method when using less base learners, especially on datasets with high feature-sample ratio.

Key words: random subspace ensemble, diversity, ensemble learning, machine learning