Joint optimisation of feature selection and SVM parameters based on an improved fireworks algorithm

In order to reduce the redundant features and improve the accuracy in classification, an improved fireworks algorithm for joint optimisation of feature selection and SVM parameters is proposed. A new fitness evaluation method is designed, which can adjust the punishment degree adaptively with the increase of the number of selected features. A differential mutation operator is introduced to enhance the information interaction among fireworks and improve the local search ability of the fireworks algorithm. A fitness-based roulette wheel selection strategy is proposed to reduce the computational complexity of the selection operator. Three groups of comparisons on 14 UCI classification datasets with increasing scales validate the effectiveness of our strategies and the significance of joint optimisation. Experimental results show that the proposed algorithm can obtain a higher accuracy in classification with fewer features.