An Empirical Study on Feature Discretization
This work addresses feature discretization for machine learning practitioners, but it is incremental as it builds on existing methods with limited experimental validation.
The paper tackled the problem of feature discretization for continuous numeric features by proposing a novel method called Local Linear Encoding (LLE), which outperformed conventional methods with fewer model parameters on two datasets.
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.