# An Ensemble Extreme Learning Machine for Data Stream Classification
## 1 Introduction  
With the explosively growing Internet and rapid development of information society, many industries have generated a large number of data streams, such as medical diagnosis, online shopping, traffic flow detection and satellite remote sensing. Different from conventional static data, data streams often have the characteristics of infinite quantity, rapid arrival, and conceptual drift, which make data stream mining faces an enormous challenges [1; 2; 3]. Since data stream classification was put forward, it has attracted much attention from scholars and made many achievements [4; 5; 6; 7; 8; 9]. Up to now, the achievements are divided into three groups: statistical analysis model, decision tree model and neural network model. In statistical analysis model, [PERSON] et al. proposed an online leaning algorithm called OAUE [10] which utilizes mean square error to determine the weight of the classification model. When the detection period is reached, the concept drift will be replaced by replacement strategy. [PERSON] et al. proposed a weighted case ensemble classification algorithm [11], and clustering algorithm is introduced to detect concept drift. If a data point does not belong to any existing class, it is considered that the class corresponding to this data may be a new concept, and then is further confirmed by data statistics in nodes. [PERSON] et al. proposed an adaptive window algorithm called HWF-ADWIN [12]. It uses Hoeffding inequality [13] to divide the nodes with the attributes corresponding to the maximum and second largest information gain to train a classifier; when the accuracy of the classifier is significantly changed, concept drift will be thought to have happened. [PERSON] et al. proposed a data stream classification method based on Kappa coefficient [14]; in the process of classification, the algorithm calculates the Kappa coefficients of each block, and detects the changes of concepts in data streams by using Kappa coefficients. When the concept of data stream is changing, the system will eliminate the classifiers which do not meet the requirements according to the existing knowledge. Compared with the contrast algorithm, this algorithm can not only obtain a higheraccuracy, but also reduce the time cost to a certain extent, and get better results. Decision tree model is very common in data stream classification tasks and there have been many publications. [PERSON] and [PERSON] et al. proposed a series of algorithms based on Hoeffding tree called VFDT and CVFDT [15; 16]; [PERSON] and [PERSON] et al. proposed semi-random decision tree algorithms [17; 18]; [PERSON] et al. proposed a red-black tree structure algorithm to improve the efficiency of finding and removing outdated nodes for imbalanced data stream classification [19]. [PERSON] et al. developed a McDiarmid Tree algorithm according to McDiarmid inequality and the threshold of the difference between the maximum information gain and the second large information gain is determined by the McDiarmid boundary [20]. With the heat of the neural network, many scholars apply neural network in data stream classification tasks. Aiming at imbalanced data stream classification [21], telecommunication fraud detection [22], spatiotemporal event streams [23] and so on, many algorithms have been proposed. However, statistical analysis model, decision tree model and neural network model need to repeatedly scan data classifiers and data several times, or there are many parameter needing to adjustment. Thus, the above drawbacks limit these models to be more widely used in data stream environment. Extreme learning machine is a single hidden layer feedforward neural network; the input weights and biases of hidden layer are randomly generated and the output weights can be automatically determined by input data [24; 25; 26; 27; 28].