Contents
What is feature selection in neural networks?
Our approach involves neural network training with an augmented cross-entropy error function. Such an approach reduces output sensitivity to the input changes. Feature selection is based on the reaction of the cross-validation data set classification error due to the removal of the individual features.
Is feature selection needed for neural network?
Neural networks are themselves often used for feature selection. This is the paradigm leading to deep learning. In that case it is unlikely you’d want to do any feature selection (except maybe whitening of the data).
Which is the simplest method for neural network selection?
Growing neurons is the simplest neurons selection algorithm. This method starts with a small number of neurons and increases the complexity until any stopping criterion is met. The algorithm returns the neural network with the optimal number of neurons obtained. 5.2. Inputs selection
How to use caret to tune a neural network?
I’d like to use the caret package in R to tune a neural network classification model on my data. In order to do this, I first have to reduce the size of the predictor-set with feature selection to make it computationally feasible, correct? Lets assume that is correct. Then the question becomes “which feature selection method?”.
How does the growing inputs method work in a neural network?
The growing inputs method calculates the correlation of every input with every output in the data set. It starts with a neural network that only contains the most correlated input and calculates the selection error for that model. It keeps adding the most correlated variables until the selection error increases.
How is a neural network like a budget?
One way to think about the process of building a predictive model (such as a neural network) is that you have a ‘budget’ of information to spend, much like a certain amount of money for a monthly household budget. With only 87 observations in your training set (and only 36 more in your test set), you have a very skimpy budget.