论文标题
有关特征离散化的实证研究
An Empirical Study on Feature Discretization
论文作者
论文摘要
在处理连续数字功能时，我们通常会采用特定功能。在这项工作中，为了找到进行特征离散化的最佳方法，我们提出了一些理论分析，其中我们着重于分析特征离散化的正确性和鲁棒性。然后，我们提出了一种称为局部线性编码（LLE）的新型离散方法。在两个数字数据集上的实验表明，LLE可以胜过更少的模型参数的常规离散方法。
When dealing with continuous numeric features, we usually adopt feature discretization. In this work, to find the best way to conduct feature discretization, we present some theoretical analysis, in which we focus on analyzing correctness and robustness of feature discretization. Then, we propose a novel discretization method called Local Linear Encoding (LLE). Experiments on two numeric datasets show that, LLE can outperform conventional discretization method with much fewer model parameters.
