I was wondering how can we use trained neural network model's weights or hidden layer output for simple classification problem, and then use those for feature engineering and implement some boosting algorithm on the new engineered features.
Suppose,if we have 100 rows with 5 features (100x5) matrix.
data: X Y x1,x2,x3,x4,x5 y1 y2 0,1,2,3,4 0 1 3,2,5,6,4 1 0
Network: 2 layers, input and softmax output, compile using cross entropy .
Can we utilise trained weights or hidden layer output of above network and use it for feature engineering on original dataset and then apply some boosting algo on modified dataset and will it increase accuracy ?