Promoter classification is the task of separating promoter from non promoter sequences. Determining promoter regions where the transcription initiation takes place is important for several reasons such as improving genome annotation and defining transcription start sites. In this study, various promoter prediction methods called ProK-means, ProSVM, and 3S1C are proposed. In ProSVM and ProK-means algorithms, structural features of DNA sequences are used to distinguish promoters from non promoters. Obtained results are compared with ProSOM which is an existing promoter prediction method. It is shown that ProSVM is able to achieve greater recall rate compared to ProSOM results. Another promoter prediction methods proposed in this study is 3S1C. The difference of the proposed technique from existing methods is using signal, similarity, structure, and context features of DNA sequences in an integrated way and a hierarchical manner. In addition to current methods related to promoter classification, the similarity feature, which compares the promoter regions between human and other species, is added to the proposed system. We show that the similarity feature improves the accuracy. To classify core promoter regions, firstly, signal, similarity, structure, and context features are extracted and then, these features are classified separately by using Support Vector Machines. Finally, output predictions are combined using multilayer perceptron. The result of 3S1C algorithm is very promising.