Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner
Algorithm 1
Input: training set
Process:
Step 1: initialize the population of cats (solutions) at random. Each solution is of length , where represents the number of features. If the corresponding feature is selected, it is represented as “1;” else, it is represented as “0.” Initialize the parameters, namely, SMP, SRD, CDC, SPC, MR, C, and R.
Step 2: calculate the fitness value of each cat (solution) using the SVM classifier, where the accuracy of the SVM classifier is considered as the fitness function. The solution that has the maximum fitness value obtained so far is considered as the best solution.
Step 3: assign the cats to perform seeking mode. Seeking mode refers to the cats at rest and its movement to the next position by looking around itself.
Step 3a: create (SMP) copies of the current cat. All the copies are considered to be candidate solutions.
Step 3a. i: if the value of SPC is true, one among the candidates retain the position, while the rest changes its position with respect to a randomly selected SRD.
Step 3a. ii: if the value of SPC is false, then all the candidates change their position by a randomly selected CDC.
Step 3b: calculate the probability of each solution being selected using Equation (2) to find the best solution that has the maximum chance to survive. If all the solutions produce the same fitness value, then the probability value is considered as “1.”
(2)
In the above formula, is the probability of the current cat , is the maximum fitness value, and is the minimum fitness value. The values of are assigned if maximum fitness has to be calculated. The values of are assigned if minimum fitness has to be calculated. In our work, the value of is assigned to .
Step 4: perform tracing mode. In this mode, the cats update their position based on the velocity. Calculate the velocity and update the position of each cat using Equation (3) and Equation (4).
(3)
(4)
In the above formula, are the position and velocities of current cat at iteration The best solution set from the cats in the population is denoted by ; denotes the dimension to be changed; is a constant, and is a random number between 0 and 1.
Step 5: update the best solution that has the maximum fitness value. If the solution in the previous iteration has low fitness value, then replace it with the current best solution; otherwise, retain the previous best solution.
Step 6: repeat step 2 to step 5 for a maximum number of iterations or until the convergence of solution is reached. The solution with the maximum fitness value obtained by the classifier is considered as the optimal feature subset.
