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 costbased method for handling prevalent class-imbalanced data, MART based feature selection for high dimension data, and different evaluation metrics for automatically determining 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 million 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 a significant decrease in economic loss each day. Internet companies such as Ant Financial and Alibaba are facing the pressing need of developing algorithms for large-scale machine learning applications, such as recommendation system [12, 22, 38] , advertising [28, 41] , and fraud detection [7, 50] , among which the detection of cash-out fraud for the financial services is a non-negligible task. Cash-out fraud is getting to be a severe threat for online credit financial firms like Ant Financial. The typical procedure of cash-out fraud can be described as the following: The shopper scans a seller-provided QR code with Alipay and pays to the seller using Ant Credit Pay (a credit service provided by Ant Financial; users can consume with the credit and pay back later), and then the shopper gets cash from the seller, and the seller can get a reward from the shopper for helping him to conduct this abnormal transaction. In this process, the shopper gets cash by consuming his credit but not money in Ant Credit Pay, and he is not aimed at buying something but to get the cash by consuming his credit. If he doesn't pay back later, this will result in an economic loss for the company. Without a proper strategy to detect and prevent fraud behavior, a huge amount of money may be lost each day from cash-out fraud, making it a serious threat. To handle this problem, more and more companies are trying the techniques of machine learning to automatically detect the potential fraud transaction. Nowadays, machine learning based methods such as logistic regression (LR) [23] and multiple additive regression trees (MART) [17] are widely employed, and these methods have brought some improvement for this task. However, a more effective machine learning algorithm is always in strong demand, since this task is closely connected with the economy, and numerous transactions are conducted by Ant Credit Pay each day. Although the amount of fraudulent transactions is only a tiny percentage of the amount of all transactions, the fraudulent transactions will still lead to serious economic loss, and a small improvement with the machine learning model means that a large amount of cash-out fraud will be detected and will bring an obvious decrease in economic loss. Thus, the exploration and deployment of new machine learning approaches for this task is always an important issue for these companies. Deep forest is a recently proposed approach which opens a new way of building deep models with non-differentiable components, especially tree ensembles [55, 56] . This new kind of deep model is shown to be able to achieve the best performance among all non-DNN methods and give competitive results with state-of-the-art DNN models in a variety of domain of tasks, including face recognition, image categorization, music classification, sentiment classification, and some low dimension data. In addition, the number of layers in the deep forest can be automatically determined according to the metric evaluated, making the model complexity adaptive with exact data (other than a pre-defined DNN structure). What's more, the deep forest approach has much fewer hyper-parameters to tune when comparing to DNN models. In fact, according to the paper, a default setting will produce highly competitive results across all these aforementioned different tasks, making it a good candidate for an off-the-shelf classifier. However, the effectiveness of this model has not been validated for extra-large-scale industrial tasks. Many features of industrial tasks, such as high dimension, class imbalance, and extremely large scale need to be taken into consideration ACM
Zhang et al. REF developed a distributed version of Deep Forest for large scale processing of data.
Distributed Deep Forest and its Application to Automatic Detection of Cash-Out Fraud
TIST
Computer Science Mathematics