使用离散化扩展预测特征集
Using Discretization for Extending the Set of Predictive Features
摘要：
迄今为止，属性离散化通常通过用离散特征的转置集合替换原始连续特征集合来执行。本文提供了一个新的想法，离散化的功能应该经常使用除了现有的功能，因此，数据集应该扩展，而不是取代，离散化的支持。我们还主张，离散化算法的开发应该有明确的目的，即用离散化值丰富非离散化数据集。我们提出了这样一个算法，D-MIAT，一个监督算法，离散化数据的基础上，少数人感兴趣的属性保留。D-MIAT仅在存在需要学习的目标值之一的强烈指示时生成新特征，因此旨在除了原始数据之外使用。我们提出了广泛的实证结果，证明使用D-MIAT的成功$ 28 $基准数据集。我们还表明，$ 10 $其他离散化算法也可以用来生成的功能，产生改进的性能时，与原始的非离散化数据组合使用。我们的研究结果表明，最好的预测性能是使用原始数据集与“标准”监督离散化算法和D-MIAT的添加功能的组合。
英文摘要：
To date, attribute discretization is typically performed by replacing the original set of continuous features with a transposed set of discrete ones. This paper provides support for a new idea that discretized features should often be used in addition to existing features and as such, datasets should be extended, and not replaced, by discretization. We also claim that discretization algorithms should be developed with the explicit purpose of enriching a non-discretized dataset with discretized values. We present such an algorithm, D-MIAT, a supervised algorithm that discretizes data based on Minority Interesting Attribute Thresholds. D-MIAT only generates new features when strong indications exist for one of the target values needing to be learned and thus is intended to be used in addition to the original data. We present extensive empirical results demonstrating the success of using D-MIAT on $ 28 $ benchmark datasets. We also demonstrate that $ 10 $ other discretization algorithms can also be used to generate features that yield improved performance when used in combination with the original non-discretized data. Our results show that the best predictive performance is attained using a combination of the original dataset with added features from a "standard" supervised discretization algorithm and D-MIAT.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
