Chaotic Particle Swarm Optimization with Mutation for Classification

In this paper, a chaotic particle swarm optimization with mutation based classifier particle swarm optimization is proposed to classifypatterns of different classes in the feature space. the introduced mutation operators and chaotic sequences allows us to overcomethe problem of early convergence into a local minima associated with particle swarm optimization algorithms. that is, the mutationoperator sharpens the convergence and it tunes the best possible solution. furthermore, to remove the irrelevant data and reduce thedimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization isintroduced. in order to demonstrate the effectiveness of our proposed classifier, mutation based classifier particle swarm optimization,it is checked out with three sets of data classifications namely, wisconsin diagnostic breast cancer, wisconsin breast cancer and heart statlog,with different feature vector dimensions. the proposed algorithm is compared with different classifier algorithms includingk-nearest neighbor, as a conventional classifier, particle swarm classifier, genetic algorithm, and imperialist competitive algorithm classifier,as more sophisticated ones. the performance of each classifier was evaluated by calculating the accuracy, sensitivity,specificity and matthews’s correlation coefficient. the experimental results show that the mutation based classifier particle swarm optimization unequivocally performs better than all the compared algorithms.