How can I improve the accuracy of my neural network on a very unbalanced dataset? I have a dataset which contains data for about accidents. The dataset consists of about 15.000 entries and I can't get more. The Distribution is as follows:


88.6% of the data are class 1 accidents
10.6% of the data are class 2 accidents
0.8% of the data are class 3 accidents


As you can see, the biggest part of the training data belongs to one class. I have only very few examples for class 3 accidents (about 100 rows out of 15.000), but it would be most important to classify class 3 accidents correctly.

I trained a pretty standard deep neural network on the data and got an accuracy of ~93% on the validation set. I used a custom Tensorflow estimator with an AdamOptimizer and tuned the parameters as good as possible. 
The Problem is, the network still classifies most of the accidents as class 1 accidents. So if I have for example 25 class 3 accidents in the validation set, the network misclassifies 10 of them as class 1. I want to improve that.

Are there any methods to improve the performance in this case? The obvious choice would be to get more data of class 3 accidents, but sadly that is not possible. 
Does it make sense to show the existing class 3 data multiple times? So for example, train 5 Epochs with all data and then 3 additional Epochs with just the class 3 accidents?

Or could I do something during the data preprocessing? Right now I'm MinMax-Scaling the input data to get to the [0, 1] interval. Is there maybe any other way to emphasize outliers more? (If you assume outliers mostly belong to class 3)

I hope someone knows some methods to increase the accuracy in this case.

EDIT:
The Dataset has mostly categorical columns like:


Street Class (e.g. highway or country road)
Light (e.g. "good")
Weather (e.g. "rainy" or "sunny")
...


Additional it has these columns:


Accident Date (just month and day)
Age 
Time of the day 
Number of injured persons
Number of Vehicles


So an entry might look like this:

{
 street_class: 'highway',
 light: 'daylight',
 date: '23. Jan',
 age: 59,
 injured_persons: 2,
 vehicles: 2,
 time: 1724,
 label: 1
 ...
}
I'd try some kind of data augmentation, but from your question is not clear what type of data you have and it's impossible to suggest a solution.

Try to add an example of the data to your question.