2019/10/17 by Mohammad Saberian, Pablo Delgado, Saberian, Mohammad +3
Computer Science · #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Face and Expression Recognition #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications
paper · pdf · doi:10.48550/arxiv.1910.09340
openalex publication_date 2019/10/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this paper we propose a method to build a neural network that is similar to an ensemble of decision trees. We first illustrate how to convert a learned ensemble of decision trees to a single neural network with one hidden layer and an input transformation. We then relax some properties of this network such as thresholds and activation functions to train an approximately equivalent decision tree ensemble. The final model, Hammock, is surprisingly simple: a fully connected two layers neural network where the input is quantized and one-hot encoded. Experiments on large and small datasets show this simple method can achieve performance similar to that of Gradient Boosted Decision Trees.