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Generative
Adversarial
Networks (GANs)
for data imbalance
Armando Vieira & Javier Rodriguez Zaurin
Alea.ai / Infinitum
Data imbalance - why it matters?
The insurance case
Myths Around
Insurance modeling
Statistics are there for a reason...
▪ Data follows Poisson / overdispersed Poisson / Gamma
distributions
▪ There are well defined quantities we can always compute
□ mean
□ standard deviation
□ skewness
▪ Data is stationary (i.e. The rate at which events occur is
constant)
▪ Events are independent (i.e. The occurrence of one event
does not affect the probability that a second event will
occur
▪ Pooling solves the issues
In Insurance only two
types of clients matter
❏ Clients that never claims
❏ Clients with large claims
By focusing on the extremes we can better
model the important factors
Imbalance
Datasets and
how to deal with
them?
Techniques to handle imbalance data
SMOTE + Tomek
GANs
Generative Adversarial Networks
10
GANs
Conditional GAN
GANs were very effective for images, but how about
tabular data?
Conditional GAN
CycleGAN
Horse Zebra
or ...
No Claim Claim
StyleGAN
Weight methods
CycleGAN for fraud detection + sparse
Autoencoders
Topology preserving CycleGAN
Results
Precision Recall F1-score
No augmentation 0.88 0.77 0.79
SMOTE 0.94 0.79 0.85
ADASYNE 0.79 0.76 0.77
cGAN 0.90 0.85 0.87
cycleGAN 0.92 0.83 0.87
Implementation Details
▪ Data has to be converted into a dense representation
▪ Domains have to be consistently related - retain same semantic
features.
▪ Loads of tricks (weight clipping, regularizers, training schedule)
▪ Consistency over time (i.e. stationary)
▪ Avoid mode collapsing
▪ Diversity enforcing with conditional batch normalization and noise
How to train GANs
▪ Small (decreasing) learning rate
▪ Different optimizers for encoder and generator
▪ Train generator multiple times for each discriminator
▪ Batch normalization and Instance Batch Normalization
Conclusions:
▪ Focus on the extremes (less data, better results)
▪ Use augmentation to alleviate imbalance data
▪ GANS can be effective under some conditions

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