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English(EN) POSSE-kNN: Pathwise Out-of-Bag Selected Subspace Ensembles for Binary Classification

新的POSSE-kNN方法提高了二分类准确性

作者 PulseAugur 编辑部 · [1 个来源] ·

一种新的机器学习方法POSSE-kNN已被开发用于二分类任务，特别是针对表格数据。这种集成技术结合了自助采样、随机特征子空间、袋外筛选和新颖的路径邻居选择。在十个基准数据集上的评估表明，与六种已建立的方法相比，POSSE-kNN在准确性、Cohen's kappa和Brier分数方面取得了更优的总体结果。 AI

影响 引入了一种新颖的集成方法，提高了表格数据分类任务的性能。

排序理由 该集群包含一篇详细介绍新机器学习算法的研究论文。

在 arXiv stat.ML 阅读 →

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新的POSSE-kNN方法提高了二分类准确性

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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Zardad Khan, Amjad Ali, Najd Adeed, Saeed Aldahmani ·

    POSSE-kNN：用于二分类的路径外包选择子空间集成

    arXiv:2211.11278v3 Announce Type: replace Abstract: Nearest neighbour classification is attractive for tabular data, but its performance can deteriorate when a fixed query centred neighbourhood does not follow the local class geometry. This study evaluates POSSE-$k$NN, a pathwise…

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