# An Empirical Study on Feature Discretization

arXiv: https://arxiv.org/abs/2004.12602 · HTML: https://mlsanity.com/paper/2004.12602/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Apr 2020.

## Built on by (most cited first, 6 of 6)

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- [Multi-Epoch Learning for Deep Click-Through Rate Prediction Models](https://mlsanity.com/paper/2305.19531/) (2023)
- [Self-supervised Graph-based Point-of-interest Recommendation](https://mlsanity.com/paper/2210.12506/) (2022)
- [Multi-Epoch learning with Data Augmentation for Deep Click-Through Rate Prediction](https://mlsanity.com/paper/2407.01607/) (2024)
- [DNN2LR: Automatic Feature Crossing for Credit Scoring](https://mlsanity.com/paper/2102.12036/) (2021)

## Abstract (arXiv)

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.