One recent area of interest in computer science is data stream management and processing. By ‘data stream’, we refer to continuous and rapidly generated packages of data. Specifi c features of data streams are immense volume, high production rate, limited data processing time, and data concept drift; these features differentiate the data stream from standard types of data. An issue for the data stream is classifi cation of input data. A novel ensemble classifi er is proposed in this paper. The classifi er uses base classifi ers of two weighting functions under different data input conditions. In addition, a new method is used to determine drift, which emphasizes the precision of the algorithm. Another characteristic of the proposed method is removal of different numbers of the base classifi ers based on their quality. Implementation of a weighting mechanism to the base classifi ers at the decision-making stage is another advantage of the algorithm. This facilitates adaptability when drifts take place, which leads to classifi ers with higher e?ciency. Furthermore, the proposed method is tested on a set of standard data and the results confi rm higher accuracy compared to available ensemble classifi ers and single classifi ers. In addition, in some cases the proposed classifi er is faster and needs less storage space.