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首页> 外文期刊>International Journal of Intelligent Systems >Scalable feature selection using ReliefF aided by locality-sensitive hashing
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Scalable feature selection using ReliefF aided by locality-sensitive hashing

机译：可扩展的特征选择使用锁骨敏感散列的Relieff辅助

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摘要

Feature selection algorithms, such as ReliefF, are very important for processing high-dimensionality data sets. However, widespread use of popular and effective such algorithms is limited by their computational cost. We describe an adaptation of the ReliefF algorithm that simplifies the costliest of its step by approximating the nearest neighbor graph using locality-sensitive hashing (LSH). The resulting ReliefF-LSH algorithm can process data sets that are too large for the original ReliefF, a capability further enhanced by distributed implementation in Apache Spark. Furthermore, ReliefF-LSH obtains better results and is more generally applicable than currently available alternatives to the original ReliefF, as it can handle regression and multiclass data sets. The fact that it does not require any additional hyperpara-meters with respect to ReliefF also avoids costly tuning. A set of experiments demonstrates the validity of this new approach and confirms its good scalability.
机译：特征选择算法（例如Relieff）对于处理高维数据集非常重要。 然而，广泛使用流行和有效的此类算法受其计算成本的限制。 我们描述了Creieff算法的适应，它通过使用位置敏感的散列（LSH）近似最近的邻居图来简化其步骤的高级性能。 由此产生的Relieff-LSH算法可以处理对于原始Relieff而言过大的数据集，通过Apache Spark中的分布式实现进一步增强的能力。 此外，Relieff-LSH获得了更好的结果，并且比目前可用的替代品更普遍适用于原始Relieff的替代方案，因为它可以处理回归和多字符数据集。 事实上，它不需要对Relieff的任何额外的繁忙仪表也避免了昂贵的调整。 一组实验表明了这种新方法的有效性，并确认了其良好的可扩展性。

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