Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner

Algorithm 4

 

Input: training set (FCSO, FKH, FBFO).

Step 1: initialize the parameters, namely, weights and bias, number of hidden layers, and learning rate of the BPNN.

Step 2: the number of hidden nodes are calculated using Equation (18).

(18)

In the above formula, is the number of hidden nodes, and is the number of input nodes.

Step 3: the input of the hidden layer is calculated using Equation (19).

(19)

In the above formula, is the input of the hidden layer; is the weights of each input nodes; is the bias.

Step 4: the output of the hidden layer is calculated using Equation (20).

(20)

where is the output of the hidden layer, and is the input to the neuron from the previous layer.

Step 5: calculate the error rate in the predicted output using Equation (21).

(21)

In the above formula, is the expected output, and is the obtained output.

Step 6: update the new weights and bias based on the learning rate and error rate using CGA.

Step 7: repeat the steps from 2 to 5 until the error rate converges.

Output: three BPNN classifiers trained using FCSO, FKH, and FBFO.