4,646 research outputs found
Implementation of complex interactions in a Cox regression framework
The standard Cox proportional hazards model has been extended by functionally describable interaction terms. The first of which are related to neural networks by adopting the idea of transforming sums of weighted covariables by means of a logistic function. A class of reasonable weight combinations within the logistic transformation is described. Apart from the standard covariable product interaction, a product of logistically transformed covariables has also been included in the analysis of performance of the new terms. An algorithm combining likelihood ratio tests and AIC criterion has been defined for model choice. The critical values of the likelihood ratio test statistics had to be corrected in order to guarantee a maximum type I error of 5% for each interaction term. The new class of interaction terms allows interpretation of functional relationships between covariables with more flexibility and can easily be implemented in standard software packages
SAFE: A Neural Survival Analysis Model for Fraud Early Detection
Many online platforms have deployed anti-fraud systems to detect and prevent
fraudulent activities. However, there is usually a gap between the time that a
user commits a fraudulent action and the time that the user is suspended by the
platform. How to detect fraudsters in time is a challenging problem. Most of
the existing approaches adopt classifiers to predict fraudsters given their
activity sequences along time. The main drawback of classification models is
that the prediction results between consecutive timestamps are often
inconsistent. In this paper, we propose a survival analysis based fraud early
detection model, SAFE, which maps dynamic user activities to survival
probabilities that are guaranteed to be monotonically decreasing along time.
SAFE adopts recurrent neural network (RNN) to handle user activity sequences
and directly outputs hazard values at each timestamp, and then, survival
probability derived from hazard values is deployed to achieve consistent
predictions. Because we only observe the user suspended time instead of the
fraudulent activity time in the training data, we revise the loss function of
the regular survival model to achieve fraud early detection. Experimental
results on two real world datasets demonstrate that SAFE outperforms both the
survival analysis model and recurrent neural network model alone as well as
state-of-the-art fraud early detection approaches.Comment: To appear in AAAI-201
A Recurrent Neural Network Survival Model: Predicting Web User Return Time
The size of a website's active user base directly affects its value. Thus, it
is important to monitor and influence a user's likelihood to return to a site.
Essential to this is predicting when a user will return. Current state of the
art approaches to solve this problem come in two flavors: (1) Recurrent Neural
Network (RNN) based solutions and (2) survival analysis methods. We observe
that both techniques are severely limited when applied to this problem.
Survival models can only incorporate aggregate representations of users instead
of automatically learning a representation directly from a raw time series of
user actions. RNNs can automatically learn features, but can not be directly
trained with examples of non-returning users who have no target value for their
return time. We develop a novel RNN survival model that removes the limitations
of the state of the art methods. We demonstrate that this model can
successfully be applied to return time prediction on a large e-commerce dataset
with a superior ability to discriminate between returning and non-returning
users than either method applied in isolation.Comment: Accepted into ECML PKDD 2018; 8 figures and 1 tabl
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