Abstract
Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFN). Because ELM has a fast speed for classification, it is widely applied in data stream classification tasks. In this paper, a new ensemble extreme learning machine is presented. Different from traditional ELM methods, a concept drift detection method is embedded; it uses online sequence learning strategy to handle gradual concept drift and uses updating classifier to deal with abrupt concept drift, so both gradual concept drift and abrupt concept drift can be detected in this paper. The experimental results showed the new ELM algorithm not only can improve the accuracy of classification result, but also can adapt to new concept in a short time.
Highlights
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
Data stream classification generally adopts a sliding window mechanism,oand several data make up a dataset called n data block and denoted Bi, where Bi = d(1), d(2), · · ·, d(n) and n is the size of data block
Waveform, letter, occupancy and protein datasets, CELM is much better than OS-Extreme learning machine (ELM); the classification performance of OS-ELM is at a low level because there are many abrupt concept drifts in those datasets
Summary
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. Bifet 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. Bilal 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. 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.
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