# An Ensemble Extreme Learning Machine for Data Stream Classification
## 1 Introduction
ELM does not need to adjust the parameters repeatedly and it has an obvious advantage in the speed of the training process comparing with the traditional neural networks [29], so it is very suitable for data stream classification tasks. [PERSON] et al. proposed an ELM algorithm based on online sequential learning mechanism called OS-ELM [30], and it extends ELM to the field of data stream classification. After OS-ELM being proposed, many scholars have proposed a series of improved OS-ELM. [PERSON] et al. proposed a timeliness online sequential extreme learning machine for timeliness problem [31]; it adopts the batch processing and weighting mechanism to make TOSELM have good stability and prediction ability. [PERSON] et al. proposed a regularization extreme learning machine with online sequential learning called OS-RELM [32]. OS-RELM combines OS-ELM and RELM [33]; at the same time, the minimum error rate is guaranteed, and the norm of the minimum weight is obtained, so that OS-RELM can have good generalization performance. [PERSON] et al. proposed a FOS-ELM with forgetting mechanism for timeliness stock data [34]. In FOS-ELM, it only uses latest data to update model, so it can avoid the invalid data to participate in updating the weights of the output layer. [PERSON] et al. proposed an ensemble online sequential extreme learning machine for imbalanced classification [35]; each OS-ELM focuses on the minority class data and is trained with a balanced subset of the data stream. For distributed multi-agent system, [PERSON] et al. proposed a online nonlinear extreme learning machine [36]; it uses optimization method to minimize empirical risk and structural risk. [PERSON] et al. applied OS-ELM in intrusion detection system [37]; before dealing with data, it introduces features selection to eliminate redundant or unrelated attributes. The above OS-ELM and its developments provide a number of ways to solve the problem of data stream classification. However, most of them lack concept drift detection mechanism; they have a good performance for data stream without concept drift or concept changing slowly, but cannot cope with the rapid change of concept in data stream. In this paper, an ensemble extreme learning machine with concept drift detection (CELM) is proposed. CELM uses manifold learning to reduce the dimensions of data and introduces concept drift detection mechanism which effectively overcomes the shortcomings of OS-ELM. The contributions of this paper are as follows:  
* An ensemble extreme learning machine algorithm is presented. In the data stream environment, the performance of ensemble classifiers is better than that of single classifier [38], so CELM employs ensemble learning method and improves the performance of ELMs. * Because data stream classification is very demanding for real time and the high dimensions of data tend to reduce the efficiency of algorithm, CELM introduces a manifold learning method to reducing the dimension of data which reduces the time consumption of CELM. * Concept drift detection is incorporated into the training process of ELM classifiers. The change of data stream is divided into three categories: normal condition, warning level and concept drift. Different from the traditional ELMs, CELM not only can detect gradual concept drift, but also can handle abrupt concept drift. The rest of this paper is organized as follows: Section 2 reviews the background knowledge of data stream classification and ELM. Section 3 states the details of ELM, and then elaborates the reducing dimension method of the manifold learning and the principles of CELM. In Section 4, CELM is compared with comparison algorithms and we discusses the experimental results. Finally, Section 5 concludes the research and gives future directions.