Boosting as a very successful classification algorithm represents a great generalization ability with appropriate ensemble diversity. It can be easily applied in the two-class classification problem. However, sequential structure prediction, in which the output is an ordered list of the labeled classes, needs to be realized by an adjusted and extended version. For that purpose the AdaBoostSeq algorithm has been introduced. It performs the multi-class classification with respect to the sequential structure of the classification target. The profile of the AdaBoostSeq algorithm is analyzed in the paper, especially its classification accuracy, using various base classifiers applied to diverse experimental datasets with comparison to other state-of-the-art methods.