用GPU加速硼达特征选择，提速同时保持精度。
Novel GPU Boruta algorithms for feature selection from high-dimensional data
- 基于置换和不纯度减少两种思路，实现GPU并行化硼达算法
- 在多个数据集上计算效率显著提升，准确率与原版相当
- 适合处理大规模高维数据，尤其适合有算力资源的研究者
大多数特征选择算法，尤其是包装器方法，由于计算复杂度高，在基于CPU的平台上运行效率低下，难以处理大规模数据集。为解决此问题，本文提出两种改进的硼达特征选择算法——Boruta-Permut（基于置换的重要性）和Boruta-TreeImp（基于不纯度减少的重要性），均通过GPU加速。在自建数据集及多个公开数据集上的实验表明，所提方法大幅提升了计算效率，且特征选择准确性与原版硼达算法相当。分析还发现，基于不纯度减少的版本会高估部分特征的重要性。总体而言，利用GPU执行硼达特征选择，为大规模数据分析提供了一种高效且成本效益高的解决方案。
原文摘要 · Abstract (English)
Most feature selection algorithms, especially wrapper methods, run inefficiently on CPU based platforms because of their high computational complexity. This inefficiency makes them unsuitable for processing large scale datasets. To address this challenge, the present study proposed two GPU accelerated versions of the Boruta feature selection procedure, in which Boruta-Permut relies on permutation based feature importance and Boruta-TreeImp employs importance based on impurity reduction. To evaluate these methods we conducted experiments on both a self constructed dataset and several publicly available datasets. The experimental results show that the proposed GPU accelerated algorithms greatly improve computational efficiency while preserving feature selection accuracy comparable to the original Boruta algorithm. In our analysis we also observe that the impurity reduction based version can overestimate the importance of some features. Overall these findings suggest that performing Boruta feature selection on GPUs offers an effective and cost efficient solution for large scale data analysis, which is a good deal.
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