This paper proposes an algorithm that implements binary encoding of the categorical features of neural network model input data, while also implementing changes in the forward and backpropagation procedures in order to achieve the property of having model weight changes, that result from the neural network learning process for certain data instances of some feature category, only affect the forward pass calculations for input data instances of that same feature category, as it is in the case of utilising one-hot encoding for categorical features.

翻译：本文提出一种算法，实现了神经网络模型输入数据中类别特征的二进制编码，同时修改前向传播与反向传播过程，使得模型对某类别特征中特定数据实例学习产生的权重变化，仅影响同一类别特征输入数据实例的前向计算——这与使用独热编码处理类别特征时的特性一致。