Can neural networks work with missing data?

Can neural networks work with missing data?

We propose a general, theoretically justified mechanism for processing missing data by neural networks. Our idea is to replace typical neuron’s response in the first hidden layer by its expected value. This approach can be applied for various types of networks at minimal cost in their modification.

What kinds of problems can neural networks approach?

Today, neural networks are used for solving many business problems such as sales forecasting, customer research, data validation, and risk management. For example, at Statsbot we apply neural networks for time-series predictions, anomaly detection in data, and natural language understanding.

How do neural networks deal with missing data?

Being creative, it is possible to model a simple missing data mechanism with a neural network. You can represent the boolean variable (like smoker, yes/no) by one input neuron, with encoded input 1 for smoker and −1 for non-smoker. Give the value 0 as input to this neuron when the smoker variable is missing.

How do you manage missing data?

Best techniques to handle missing data

  1. Use deletion methods to eliminate missing data. The deletion methods only work for certain datasets where participants have missing fields.
  2. Use regression analysis to systematically eliminate data.
  3. Data scientists can use data imputation techniques.

What are the applications of neural network?

Medicine, Electronic Nose, Security, and Loan Applications – These are some applications that are in their proof-of-concept stage, with the acception of a neural network that will decide whether or not to grant a loan, something that has already been used more successfully than many humans.

What is neural Network example?

Neural networks are designed to work just like the human brain does. In the case of recognizing handwriting or facial recognition, the brain very quickly makes some decisions. For example, in the case of facial recognition, the brain might start with “It is female or male?

How to encode date as input in neural network?

For every instance you actually care about (events in the future, the past already happened) the time variable will take on a value that is greater than any value the time variable will take in your training data. Such a variable is very unlikely to help.

How does the order you feed a neural network affect the predictions?

With a standard feedforward neural network the order you feed the network your data is going to have no impact on the predictions. Order may impact training if you’re using stochastic or mini-batch gradient descent, but this is only an artifact of the iterative (as opposed to batch) training method.

How to model temporal dependence in a neural network?

If you want to model temporal dependence with a neural network you’ll need to use something like a sliding window or a recurrent neural network.