# Distributed Deep Forest and its Application to Automatic Detection of Cash-out Fraud

arXiv: https://arxiv.org/abs/1805.04234 · HTML: https://mlsanity.com/paper/1805.04234/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2018.

## Built on by (most cited first, 7 of 7)

- [On Explaining Random Forests with SAT](https://mlsanity.com/paper/2105.10278/) (2021)
- [Locally-Minimal Probabilistic Explanations](https://mlsanity.com/paper/2312.11831/) (2023)
- [Graph Mining for Cybersecurity: A Survey](https://mlsanity.com/paper/2304.00485/) (2023)
- [Financial Default Prediction via Motif-preserving Graph Neural Network with Curriculum Learning](https://mlsanity.com/paper/2403.06482/) (2024)
- [Interpreting Deep Forest through Feature Contribution and MDI Feature Importance](https://mlsanity.com/paper/2305.00805/) (2023)
- [ALT: An Automatic System for Long Tail Scenario Modeling](https://mlsanity.com/paper/2305.11390/) (2023)
- [The Role of Depth, Width, and Tree Size in Expressiveness of Deep Forest](https://mlsanity.com/paper/2407.05108/) (2024)

## Abstract (arXiv)

Internet companies are facing the need for handling large-scale machine learning applications on a daily basis and distributed implementation of machine learning algorithms which can handle extra-large scale tasks with great performance is widely needed. Deep forest is a recently proposed deep learning framework which uses tree ensembles as its building blocks and it has achieved highly competitive results on various domains of tasks. However, it has not been tested on extremely large scale tasks. In this work, based on our parameter server system, we developed the distributed version of deep forest. To meet the need for real-world tasks, many improvements are introduced to the original deep forest model, including MART (Multiple Additive Regression Tree) as base learners for efficiency and effectiveness consideration, the cost-based method for handling prevalent class-imbalanced data, MART based feature selection for high dimension data and different evaluation metrics for automatically determining of the cascade level. We tested the deep forest model on an extra-large scale task, i.e., automatic detection of cash-out fraud, with more than 100 millions of training samples. Experimental results showed that the deep forest model has the best performance according to the evaluation metrics from different perspectives even with very little effort for parameter tuning. This model can block fraud transactions in a large amount of money each day. Even compared with the best-deployed model, the deep forest model can additionally bring into a significant decrease in economic loss each day.