Machine learning algorithms are used in various application and the need for faster and more accurate algorithms is urgent. Classification problem, as one of the most common machine learning tasks, has numerous proposed algorithms for solving it. One of the main factors that affects the classification accuracy, regardless of the used classifier, is the chosen feature set. Due to the fact that the classification quality depends on the features, feature selection represents an important task in machine learning. In this paper we propose adjusted bare bone fireworks algorithm for feature selection. Support vector machine is used as classifier, thus we additionally added SVM parameter optimization. The proposed method is tested on standard benchmark classification datasets from the UCI repository and the results are compared with other swarm intelligence methods. The results show that the proposed method achieves higher accuracy compared to the other methods even without SVM parameters tuning. As expected, parameter tuning has additionally increased the classification accuracy.